Machine learning in bioinformatics
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Machine learning in bioinformatics is the application of
machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
algorithms to
bioinformatics Bioinformatics () is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combi ...
, including
genomics Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dim ...
,
proteomics Proteomics is the large-scale study of proteins. Proteins are vital parts of living organisms, with many functions such as the formation of structural fibers of muscle tissue, enzymatic digestion of food, or synthesis and replication of DNA. In ...
,
microarrays A microarray is a multiplex lab-on-a-chip. Its purpose is to simultaneously detect the expression of thousands of genes from a sample (e.g. from a tissue). It is a two-dimensional array on a solid substrate—usually a glass slide or silicon t ...
,
systems biology Systems biology is the computational modeling, computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological syst ...
,
evolution Evolution is change in the heritable characteristics of biological populations over successive generations. These characteristics are the expressions of genes, which are passed on from parent to offspring during reproduction. Variation ...
, and
text mining Text mining, also referred to as ''text data mining'', similar to text analytics, is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extract ...
. Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as
protein structure prediction Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different ...
, this proved difficult. Machine learning techniques, such as
deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. De ...
can learn features of data sets, instead of requiring the programmer to define them individually. The algorithm can further learn how to combine low-level
features Feature may refer to: Computing * Feature (CAD), could be a hole, pocket, or notch * Feature (computer vision), could be an edge, corner or blob * Feature (software design) is an intentional distinguishing characteristic of a software item ...
into more abstract features, and so on. This multi-layered approach allows such systems to make sophisticated predictions when appropriately trained. These methods contrast with other
computational biology Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has fo ...
approaches which, while exploiting existing datasets, do not allow the data to be interpreted and analyzed in unanticipated ways. In recent years, the size and number of available biological datasets have skyrocketed.


Tasks

Machine learning algorithms in bioinformatics can be used for prediction, classification, and feature selection. Methods to achieve this task are varied and span many disciplines; most well known among them are machine learning and statistics. Classification and prediction tasks aim at building models that describe and distinguish classes or concepts for future prediction. The differences between them are the following: * Classification/recognition outputs a categorical class, while prediction outputs a numerical valued feature. * The type of algorithm, or process used to build the predictive models from data using analogies, rules, neural networks, probabilities, and/or statistics. Due to the exponential growth of information technologies and applicable models, including artificial intelligence and data mining, in addition to the access ever-more comprehensive data sets, new and better information analysis techniques have been created, based on their ability to learn. Such models allow reach beyond description and provide insights in the form of testable models.


Machine learning approaches


Artificial neural networks

Artificial neural network Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ...
s in bioinformatics have been used for: * Comparing and aligning RNA, protein, and DNA sequences. * Identification of promoters and finding genes from sequences related to DNA. * Interpreting the expression-gene and micro-array data. * Identifying the network (regulatory) of genes. * Learning evolutionary relationships by constructing
phylogenetic tree A phylogenetic tree (also phylogeny or evolutionary tree Felsenstein J. (2004). ''Inferring Phylogenies'' Sinauer Associates: Sunderland, MA.) is a branching diagram or a tree showing the evolutionary relationships among various biological spec ...
s. * Classifying and predicting
protein structure Protein structure is the three-dimensional arrangement of atoms in an amino acid-chain molecule. Proteins are polymers specifically polypeptides formed from sequences of amino acids, the monomers of the polymer. A single amino acid monomer ma ...
. * Molecular design and docking.


Feature engineering

The way that features, often vectors in a many-dimensional space, are extracted from the domain data is an important component of learning systems. In genomics, a typical representation of a sequence is a vector of
k-mer In bioinformatics, ''k''-mers are substrings of length k contained within a biological sequence. Primarily used within the context of computational genomics and sequence analysis, in which ''k''-mers are composed of nucleotides (''i.e''. A, T, G ...
s frequencies, which is a vector of dimension 4^k whose entries count the appearance of each subsequence of length k in a given sequence. Since for a value as small as k=12 the dimensionality of these vectors is huge (e.g. in this case the dimension is 4^ \approx 16\times 10^6), techniques such as
principal component analysis Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and ...
are used to project the data to a lower dimensional space, thus selecting a smaller set of features from the sequences.


Classification

In this type of machine learning task, the output is a discrete variable. One example of this type of task in bioinformatics is labeling new genomic data (such as genomes of unculturable bacteria) based on a model of already labeled data.


Hidden Markov models

Hidden Markov models A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it X — with unobservable ("''hidden''") states. As part of the definition, HMM requires that there be an ob ...
(HMMs) are a class of
statistical models A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, ...
for sequential data (often related to systems evolving over time). An HMM is composed of two mathematical objects: an observed state‐dependent process X_1,X_2,\ldots ,X_M, and an unobserved (hidden) state process S_1,S_2,\ldots ,S_T. In an HMM, the state process is not directly observed – it is a 'hidden' (or 'latent') variable – but observations are made of a state‐dependent process (or observation process) that is driven by the underlying state process (and which can thus be regarded as a noisy measurement of the system states of interest). HMMs can be formulated in continuous time. HMMs can be used to profile and convert a multiple sequence alignment into a position-specific scoring system suitable for searching databases for homologous sequences remotely. Additionally, ecological phenomena can be described by HMMs.


Convolutional neural networks

Convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...
s (CNN) are a class of
deep neural network Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. De ...
whose architecture is based on shared weights of convolution kernels or filters that slide along input features, providing translation-equivariant responses known as feature maps. CNNs take advantage of the hierarchical pattern in data and assemble patterns of increasing complexity using smaller and simpler patterns discovered via their filters. Therefore, they are lower on a scale of connectivity and complexity. Convolutional networks were inspired by
biological Biology is the scientific study of life. It is a natural science with a broad scope but has several unifying themes that tie it together as a single, coherent field. For instance, all organisms are made up of cells that process hereditary in ...
processes in that the connectivity pattern between
neurons A neuron, neurone, or nerve cell is an electrically excitable cell that communicates with other cells via specialized connections called synapses. The neuron is the main component of nervous tissue in all animals except sponges and placozoa. N ...
resembles the organization of the animal
visual cortex The visual cortex of the brain is the area of the cerebral cortex that processes visual information. It is located in the occipital lobe. Sensory input originating from the eyes travels through the lateral geniculate nucleus in the thalamus and ...
. Individual
cortical neuron The cerebral cortex, also known as the cerebral mantle, is the outer layer of neural tissue of the cerebrum of the brain in humans and other mammals. The cerebral cortex mostly consists of the six-layered neocortex, with just 10% consisting of ...
s respond to stimuli only in a restricted region of the
visual field The visual field is the "spatial array of visual sensations available to observation in introspectionist psychological experiments". Or simply, visual field can be defined as the entire area that can be seen when an eye is fixed straight at a point ...
known as the
receptive field The receptive field, or sensory space, is a delimited medium where some physiological stimuli can evoke a sensory neuronal response in specific organisms. Complexity of the receptive field ranges from the unidimensional chemical structure of od ...
. The receptive fields of different neurons partially overlap such that they cover the entire visual field. CNN uses relatively little pre-processing compared to other image classification algorithms. This means that the network learns to optimize the
filters Filter, filtering or filters may refer to: Science and technology Computing * Filter (higher-order function), in functional programming * Filter (software), a computer program to process a data stream * Filter (video), a software component tha ...
(or kernels) through automated learning, whereas in traditional algorithms these filters are hand-engineered. This independence from prior knowledge and human intervention in feature extraction is a major advantage.


Phylogenetic convolutional neural networks

A phylogenetic convolutional neural network (Ph-CNN) is a novel
convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...
architecture proposed by Fioranti et al. to classify
metagenomics Metagenomics is the study of genetic material recovered directly from environmental or clinical samples by a method called sequencing. The broad field may also be referred to as environmental genomics, ecogenomics, community genomics or microb ...
data. In this approach, phylogenetic data is endowed with patristic distance (the sum of the lengths of all branches connecting two
operational taxonomic unit An Operational Taxonomic Unit (OTU) is an operational definition used to classify groups of closely related individuals. The term was originally introduced in 1963 by Robert R. Sokal and Peter H. A. Sneath in the context of numerical taxonomy, w ...
s TU to select k-neighborhoods for each OTU, and each OTU and its neighbors are processed with convolutional filters. Ph-CNN achieves promising results compared to fully connected neural networks, random forest and support vector machines.


Random forest

Random forest Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of th ...
s (RF) classify by constructing an ensemble of
decision trees A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...
, and outputting the average prediction of the individual trees. This is a modification of
bootstrap aggregating Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regress ...
(which aggregates a large collection of decision trees) and can be used for
classification Classification is a process related to categorization, the process in which ideas and objects are recognized, differentiated and understood. Classification is the grouping of related facts into classes. It may also refer to: Business, organizat ...
or regression. As random forests give an internal estimate of generalization error, cross-validation is unnecessary. In addition, they produce proximities, which can be used to impute missing values, and which enable novel data visualizations. Computationally, random forests are appealing because they naturally handle both regression and (multiclass) classification, are relatively fast to train and to predict, depend only on one or two tuning parameters, have a built-in estimate of the generalization error, can be used directly for high-dimensional problems, and can easily be implemented in parallel. Statistically, random forests are appealing for additional features, such as measures of variable importance, differential class weighting, missing value imputation, visualization, outlier detection, and unsupervised learning.


Clustering

Clustering - the partitioning of a data set into disjoint subsets, so that the data in each subset are as close as possible to each other and as distant as possible from data in any other subset, according to some defined
distance Distance is a numerical or occasionally qualitative measurement of how far apart objects or points are. In physics or everyday usage, distance may refer to a physical length or an estimation based on other criteria (e.g. "two counties over"). ...
or similarity function - is a common technique for statistical data analysis. Clustering is central to much data-driven bioinformatics research and serves as a powerful computational method whereby means of hierarchical, centroid-based, distribution-based, density-based, and self-organizing maps classification, has long been studied and used in classical machine learning settings. Particularly, clustering helps to analyze unstructured and high-dimensional data in the form of sequences, expressions, texts, images, and so on. Clustering is also used to gain insights into biological processes at the
genomic Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dim ...
level, e.g. gene functions, cellular processes, subtypes of cells,
gene regulation Regulation of gene expression, or gene regulation, includes a wide range of mechanisms that are used by cells to increase or decrease the production of specific gene products (protein or RNA). Sophisticated programs of gene expression are wide ...
, and metabolic processes.


Clustering algorithms used in bioinformatics

Data clustering algorithms can be hierarchical or partitional. Hierarchical algorithms find successive clusters using previously established clusters, whereas partitional algorithms determine all clusters at once. Hierarchical algorithms can be agglomerative (bottom-up) or divisive (top-down). Agglomerative algorithms begin with each element as a separate cluster and merge them in successively larger clusters. Divisive algorithms begin with the whole set and proceed to divide it into successively smaller clusters.
Hierarchical clustering In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into ...
is calculated using metrics on
Euclidean spaces Euclidean space is the fundamental space of geometry, intended to represent physical space. Originally, that is, in Euclid's ''Elements'', it was the three-dimensional space of Euclidean geometry, but in modern mathematics there are Euclidean s ...
, the most commonly used is the
Euclidean distance In mathematics, the Euclidean distance between two points in Euclidean space is the length of a line segment between the two points. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem, therefor ...
computed by finding the square of the difference between each variable, adding all the squares, and finding the square root of the said sum. An example of a
hierarchical clustering In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into ...
algorithm is
BIRCH A birch is a thin-leaved deciduous hardwood tree of the genus ''Betula'' (), in the family Betulaceae, which also includes alders, hazels, and hornbeams. It is closely related to the beech-oak family Fagaceae. The genus ''Betula'' contains 30 ...
, which is particularly good on bioinformatics for its nearly linear
time complexity In computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by ...
given generally large datasets. Partitioning algorithms are based on specifying an initial number of groups, and iteratively reallocating objects among groups to convergence. This algorithm typically determines all clusters at once. Most applications adopt one of two popular heuristic methods:
k-means ''k''-means clustering is a method of vector quantization, originally from signal processing, that aims to partition ''n'' observations into ''k'' clusters in which each observation belongs to the cluster with the nearest mean (cluster centers o ...
algorithm or
k-medoids The -medoids problem is a clustering problem similar to -means. The name was coined by Leonard Kaufman and Peter J. Rousseeuw with their PAM algorithm. Both the -means and -medoids algorithms are partitional (breaking the dataset up into group ...
. Other algorithms do not require an initial number of groups, such as
affinity propagation In statistics and data mining, affinity propagation (AP) is a Cluster analysis, clustering algorithm based on the concept of "message passing" between data points. Unlike clustering algorithms such as K-means clustering, -means or K-medoids, -medoid ...
. In a genomic setting this algorithm has been used both to cluster biosynthetic gene clusters in gene cluster families(GCF) and to cluster said GCFs.


Workflow

Typically, a workflow for applying machine learning to biological data goes through four steps: * ''Recording,'' including capture and storage. In this step, different information sources may be merged into a single set. * ''Preprocessing,'' including cleaning and restructuring into a ready-to-analyze form. In this step, uncorrected data are eliminated or corrected, while missing data maybe imputed and relevant variables chosen. * ''Analysis,'' evaluating data using either supervised or unsupervised algorithms. The algorithm is typically trained on a subset of data, optimizing parameters, and evaluated on a separate test subset. * ''Visualization and interpretation,'' where knowledge is represented effectively using different methods to assess the significance and importance of the findings.


Data errors

* Duplicate data is a significant issue in bioinformatics. Publicly available data may be of uncertain quality. * Errors during experimentation. * Erroneous interpretation. * Typing mistakes. * Non-standardized methods (3D structure in PDB from multiple sources, X-ray diffraction, theoretical modeling, nuclear magnetic resonance, etc.) are used in experiments.


Applications

In general, a machine learning system can usually be trained to recognize elements of a certain class given sufficient samples. For example, machine learning methods can be trained to identify specific visual features such as splice sites.
Support vector machines In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratorie ...
have been extensively used in cancer genomic studies. In addition,
deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. De ...
has been incorporated into bioinformatic algorithms. Deep learning applications have been used for regulatory genomics and cellular imaging. Other applications include medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Deep learning has been applied to regulatory genomics, variant calling and pathogenicity scores.
Natural language processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to pro ...
and
text mining Text mining, also referred to as ''text data mining'', similar to text analytics, is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extract ...
have helped to understand phenomena including protein-protein interaction, gene-disease relation as well as predicting biomolecule structures and functions.


Precision/personalized medicine

Natural language processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to pro ...
algorithms
personalized medicine Personalized medicine, also referred to as precision medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on the ...
for patients who suffer genetic diseases, by combining the extraction of clinical information and genomic data available from the patients. Institutes such as Health-funded Pharmacogenomics Research Network focus on finding breast cancer treatments.
Precision medicine Precision, precise or precisely may refer to: Science, and technology, and mathematics Mathematics and computing (general) * Accuracy and precision, measurement deviation from true value and its scatter * Significant figures, the number of digit ...
considers individual genomic variability, enabled by large-scale biological databases. Machine learning can be applied to perform the matching function between (groups of patients) and specific treatment modalities. Computational techniques are used to solve other problems, such as efficient primer design for PCR, biological-image analysis and back translation of proteins (which is, given the degeneration of the genetic code, a complex combinatorial problem).


Genomics

While genomic sequence data has historically been sparse due to the technical difficulty of sequencing a piece of DNA, the number of available sequences is growing exponentially. However, while
raw data Raw data, also known as primary data, are ''data'' (e.g., numbers, instrument readings, figures, etc.) collected from a source. In the context of examinations, the raw data might be described as a raw score (after test scores). If a scientist ...
is becoming increasingly available and accessible, biological interpretation of this data is occurring at a much slower pace. This makes for an increasing need for developing
computational genomics Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data (i.e., experimental data obtai ...
tools, including machine learning systems, that can automatically determine the location of protein-encoding genes within a given DNA sequence (i.e.
gene prediction In computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functiona ...
). Gene prediction is commonly performed through both ''extrinsic searches'' and ''intrinsic searches''. For the extrinsic search, the input DNA sequence is run through a large database of sequences whose genes have been previously discovered and their locations annotated and identifying the target sequence's genes by determining which strings of bases within the sequence are homologous to known gene sequences. However, not all the genes in a given input sequence can be identified through homology alone, due to limits in the size of the database of known and annotated gene sequences. Therefore, an intrinsic search is needed where a gene prediction program attempts to identify the remaining genes from the DNA sequence alone. Machine learning has also been used for the problem of
multiple sequence alignment Multiple sequence alignment (MSA) may refer to the process or the result of sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. In many cases, the input set of query sequences are assumed to have an evolutio ...
which involves aligning many DNA or amino acid sequences in order to determine regions of similarity that could indicate a shared evolutionary history. It can also be used to detect and visualize genome rearrangements.


Proteomics

Protein Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, respo ...
s, strings of
amino acid Amino acids are organic compounds that contain both amino and carboxylic acid functional groups. Although hundreds of amino acids exist in nature, by far the most important are the alpha-amino acids, which comprise proteins. Only 22 alpha am ...
s, gain much of their function from
protein folding Protein folding is the physical process by which a protein chain is translated to its native three-dimensional structure, typically a "folded" conformation by which the protein becomes biologically functional. Via an expeditious and reproduci ...
, where they conform into a three-dimensional structure, including the
primary structure Protein primary structure is the linear sequence of amino acids in a peptide or protein. By convention, the primary structure of a protein is reported starting from the amino-terminal (N) end to the carboxyl-terminal (C) end. Protein biosynthes ...
, the
secondary structure Protein secondary structure is the three dimensional conformational isomerism, form of ''local segments'' of proteins. The two most common Protein structure#Secondary structure, secondary structural elements are alpha helix, alpha helices and beta ...
(
alpha helices The alpha helix (α-helix) is a common motif in the secondary structure of proteins and is a right hand-helix conformation in which every backbone N−H group hydrogen bonds to the backbone C=O group of the amino acid located four residues ear ...
and
beta sheet The beta sheet, (β-sheet) (also β-pleated sheet) is a common motif of the regular protein secondary structure. Beta sheets consist of beta strands (β-strands) connected laterally by at least two or three backbone hydrogen bonds, forming a g ...
s), the
tertiary structure Protein tertiary structure is the three dimensional shape of a protein. The tertiary structure will have a single polypeptide chain "backbone" with one or more protein secondary structures, the protein domains. Amino acid side chains may int ...
, and the quartenary structure. Protein secondary structure prediction is a main focus of this subfield as tertiary and quartenary structures are determined based on the secondary structure. Solving the true structure of a protein is expensive and time-intensive, furthering the need for systems that can accurately predict the structure of a protein by analyzing the amino acid sequence directly. Prior to machine learning, researchers needed to conduct this prediction manually. This trend began in 1951 when Pauling and Corey released their work on predicting the hydrogen bond configurations of a protein from a polypeptide chain. Automatic feature learning reaches an accuracy of 82-84%. The current state-of-the-art in secondary structure prediction uses a system called DeepCNF (deep convolutional neural fields) which relies on the machine learning model of
artificial neural network Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ...
s to achieve an accuracy of approximately 84% when tasked to classify the amino acids of a protein sequence into one of three structural classes (helix, sheet, or coil). The theoretical limit for three-state protein secondary structure is 88–90%. Machine learning has also been applied to proteomics problems such as protein side-chain prediction, protein loop modeling, and
protein contact map A protein contact map represents the distance between all possible amino acid residue pairs of a three-dimensional protein structure using a binary two-dimensional matrix. For two residues i and j, the ij element of the matrix is 1 if the two resi ...
prediction.


Metagenomics

Metagenomics Metagenomics is the study of genetic material recovered directly from environmental or clinical samples by a method called sequencing. The broad field may also be referred to as environmental genomics, ecogenomics, community genomics or microb ...
is the study of microbial communities from environmental DNA samples. Currently, limitations and challenges predominate in the implementation of machine learning tools due to the amount of data in environmental samples. Supercomputers and web servers have made access to these tools easier. The high dimensionality of microbiome datasets is a major challenge in studying the microbiome; this significantly limits the power of current approaches for identifying true differences and increases the chance of false discoveries. Despite their importance, machine learning tools related to metagenomics have focused on the study of gut microbiota and the relationship with digestive diseases, such as
inflammatory bowel disease Inflammatory bowel disease (IBD) is a group of inflammation, inflammatory conditions of the colon (anatomy), colon and small intestine, Crohn's disease and ulcerative colitis being the principal types. Crohn's disease affects the small intestine a ...
(IBD), ''
Clostridioides difficile ''Clostridioides difficile'' ( syn. ''Clostridium difficile'') is a bacterium that is well known for causing serious diarrheal infections, and may also cause colon cancer. Also known as ''C. difficile'', or ''C. diff'' (), is Gram-positive spe ...
'' infection (CDI),
colorectal cancer Colorectal cancer (CRC), also known as bowel cancer, colon cancer, or rectal cancer, is the development of cancer from the colon or rectum (parts of the large intestine). Signs and symptoms may include blood in the stool, a change in bowel m ...
and
diabetes Diabetes, also known as diabetes mellitus, is a group of metabolic disorders characterized by a high blood sugar level ( hyperglycemia) over a prolonged period of time. Symptoms often include frequent urination, increased thirst and increased ap ...
, seeking better diagnosis and treatments. Many algorithms were developed to classify microbial communities according to the health condition of the host, regardless of the type of sequence data, e.g.
16S rRNA 16S rRNA may refer to: * 16S ribosomal RNA 16 S ribosomal RNA (or 16 S rRNA) is the RNA component of the 30S subunit of a prokaryotic ribosome ( SSU rRNA). It binds to the Shine-Dalgarno sequence and provides most of the SSU structure. The g ...
or
whole-genome sequencing Whole genome sequencing (WGS), also known as full genome sequencing, complete genome sequencing, or entire genome sequencing, is the process of determining the entirety, or nearly the entirety, of the DNA sequence of an organism's genome at a ...
(WGS), using methods such as least absolute shrinkage and selection operator classifier,
random forest Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of th ...
, supervised classification model, and gradient boosted tree model.
Neural networks A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...
, such as
recurrent neural network A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes. This allows it to exhibit temporal dynamic ...
s (RNN),
convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...
s (CNN), and
Hopfield neural network A Hopfield network (or Ising model of a neural network or Ising–Lenz–Little model) is a form of recurrent artificial neural network and a type of spin glass system popularised by John Hopfield in 1982 as described earlier by Little in 1974 ba ...
s have been added. For example, in 2018, Fioravanti et al. developed an algorithm called Ph-CNN to classify data samples from healthy patients and patients with IBD symptoms (to distinguish healthy and sick patients) by using phylogenetic trees and convolutional neural networks. In addition,
random forest Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of th ...
(RF) methods and implemented importance measures help in the identification of microbiome species that can be used to distinguish diseased and non-diseased samples. However, the performance of a decision tree and the diversity of decision trees in the ensemble significantly influence the performance of RF algorithms. The generalization error for RF measures how accurate the individual classifiers are and their interdependence. Therefore, the high dimensionality problems of microbiome datasets pose challenges. Effective approaches require many possible variable combinations, which exponentially increases the computational burden as the number of features increases. For microbiome analysis in 2020 Dang & Kishino developed a novel analysis pipeline. The core of the pipeline is an RF classifier coupled with forwarding
variable selection In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construc ...
(RF-FVS), which selects a minimum-size core set of microbial species or functional signatures that maximize the predictive classifier performance. The framework combines: * identifying a few significant features by a massively parallel forward
variable selection In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construc ...
procedure * mapping the selected species on a
phylogenetic tree A phylogenetic tree (also phylogeny or evolutionary tree Felsenstein J. (2004). ''Inferring Phylogenies'' Sinauer Associates: Sunderland, MA.) is a branching diagram or a tree showing the evolutionary relationships among various biological spec ...
, and * predicting functional profiles by functional gene enrichment analysis from metagenomic
16S rRNA 16S rRNA may refer to: * 16S ribosomal RNA 16 S ribosomal RNA (or 16 S rRNA) is the RNA component of the 30S subunit of a prokaryotic ribosome ( SSU rRNA). It binds to the Shine-Dalgarno sequence and provides most of the SSU structure. The g ...
data. They demonstrated performance by analyzing two published datasets from large-scale case-control studies: * 16S rRNA gene amplicon data for ''C. difficile'' infection (CDI) and * shotgun metagenomics data for human colorectal cancer (CRC). The proposed approach improved the accuracy from 81% to 99.01% for CDI and from 75.14% to 90.17% for CRC. The use of machine learning in environmental samples has been less explored, maybe because of data complexity, especially from WGS. Some works show that it is possible to apply these tools in environmental samples. In 2021 Dhungel et al., designed an R package called MegaR. This package allows working with 16S rRNA and whole metagenomic sequences to make taxonomic profiles and classification models by machine learning models. MegaR includes a comfortable visualization environment to improve the user experience. Machine learning in environmental metagenomics can help to answer questions related to the interactions between microbial communities and ecosystems, e.g. the work of Xun et al., in 2021 where the use of different machine learning methods offered insights on the relationship among the soil, microbiome biodiversity, and ecosystem stability.


Microarrays

Microarray A microarray is a multiplex lab-on-a-chip. Its purpose is to simultaneously detect the expression of thousands of genes from a sample (e.g. from a tissue). It is a two-dimensional array on a solid substrate—usually a glass slide or silicon t ...
s, a type of
lab-on-a-chip A lab-on-a-chip (LOC) is a device that integrates one or several laboratory functions on a single integrated circuit (commonly called a "chip") of only millimeters to a few square centimeters to achieve automation and high-throughput screening. ...
, are used for automatically collecting data about large amounts of biological material. Machine learning can aid in analysis, and has been applied to expression pattern identification, classification, and genetic network induction. This technology is especially useful for monitoring gene expression, aiding in diagnosing cancer by examining which genes are expressed. One of the main tasks is identifying which genes are expressed based on the collected data. In addition, due to the huge number of genes on which data is collected by the microarray, winnowing the large amount of irrelevant data to the task of expressed gene identification is challenging. Machine learning presents a potential solution as various classification methods can be used to perform this identification. The most commonly used methods are
radial basis function network In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inp ...
s,
deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. De ...
, Bayesian classification,
decision tree A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...
s, and
random forest Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of th ...
.


Systems biology

Systems biology focuses on the study of emergent behaviors from complex interactions of simple biological components in a system. Such components can include DNA, RNA, proteins, and metabolites. Machine learning has been used to aid in modeling these interactions in domains such as genetic networks, signal transduction networks, and metabolic pathways. Probabilistic graphical models, a machine learning technique for determining the relationship between different variables, are one of the most commonly used methods for modeling genetic networks. In addition, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using Markov chain optimization.
Genetic algorithm In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to gene ...
s, machine learning techniques which are based on the natural process of evolution, have been used to model genetic networks and regulatory structures. Other systems biology applications of machine learning include the task of enzyme function prediction, high throughput microarray data analysis, analysis of genome-wide association studies to better understand markers of disease, protein function prediction.


Evolution

This domain, particularly
phylogenetic tree A phylogenetic tree (also phylogeny or evolutionary tree Felsenstein J. (2004). ''Inferring Phylogenies'' Sinauer Associates: Sunderland, MA.) is a branching diagram or a tree showing the evolutionary relationships among various biological spec ...
reconstruction, uses the features of machine learning techniques. Phylogenetic trees are schematic representations of the evolution of organisms. Initially, they were constructed using features such as morphological and metabolic features. Later, due to the availability of genome sequences, the construction of the phylogenetic tree algorithm used the concept based on genome comparison. With the help of optimization techniques, a comparison was done by means of multiple sequence alignment.


Stroke diagnosis

Machine learning methods for the analysis of
neuroimaging Neuroimaging is the use of quantitative (computational) techniques to study the structure and function of the central nervous system, developed as an objective way of scientifically studying the healthy human brain in a non-invasive manner. Incre ...
data are used to help diagnose
stroke A stroke is a medical condition in which poor blood flow to the brain causes cell death. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Both cause parts of the brain to stop functionin ...
. Historically multiple approaches to this problem involved neural networks. Multiple approaches to detect strokes used machine learning. As proposed by Mirtskhulava, feed-forward networks were tested to detect strokes using neural imaging. As proposed by Titano 3D-CNN techniques were tested in supervised classification to screen head CT images for acute neurologic events. Three-dimensional
CNN CNN (Cable News Network) is a multinational cable news channel headquartered in Atlanta, Georgia, U.S. Founded in 1980 by American media proprietor Ted Turner and Reese Schonfeld as a 24-hour cable news channel, and presently owned by ...
and SVM methods are often used.


Text mining

The increase in biological publications increased the difficulty in searching and compiling relevant available information on a given topic. This task is known as
knowledge extraction Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must r ...
. It is necessary for biological data collection which can then in turn be fed into machine learning algorithms to generate new biological knowledge. Machine learning can be used for this knowledge extraction task using techniques such as
natural language processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to pro ...
to extract the useful information from human-generated reports in a database. Text Nailing, an alternative approach to machine learning, capable of extracting features from clinical narrative notes was introduced in 2017. This technique has been applied to the search for novel drug targets, as this task requires the examination of information stored in biological databases and journals. Annotations of proteins in protein databases often do not reflect the complete known set of knowledge of each protein, so additional information must be extracted from biomedical literature. Machine learning has been applied to the automatic annotation of gene and protein function, determination of the protein subcellular localization, DNA-expression array analysis, large-scale
protein interaction Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, respo ...
analysis, and molecule interaction analysis. Another application of text mining is the detection and visualization of distinct DNA regions given sufficient reference data.


Clustering and abundance profiling of BGCs

Microbial communities are complex assembles of diverse microorganisms, where symbiont partners constantly produce diverse metabolites derived from the primary and secondary (specialized) metabolism, from which metabolism plays an important role in microbial interaction. Metagenomic and metatranscriptomic data are an important source for deciphering communications signals. Molecular mechanisms produce specialized metabolites in various ways. Biosynthetic Gene Clusters (BGCs) attract attention, since several metabolites are clinically valuable, anti-microbial, anti-fungal, anti-parasitic, anti-tumor and immunosuppressive agents produced by the modular action of multi-enzymatic, multi-domains gene clusters, such as
Nonribosomal peptide Nonribosomal peptides (NRP) are a class of peptide secondary metabolites, usually produced by microorganisms like bacteria and fungi. Nonribosomal peptides are also found in higher organisms, such as nudibranchs, but are thought to be made by bacter ...
synthetases (NRPSs) and
polyketide synthase Polyketides are a class of natural products derived from a precursor molecule consisting of a chain of alternating ketone (or reduced forms of a ketone) and methylene groups: (-CO-CH2-). First studied in the early 20th century, discovery, biosynth ...
s (PKSs). Diverse studies show that grouping BGCs that share homologous core genes into gene cluster families (GCFs) can yield useful insights into the chemical diversity of the analyzed strains, and can support linking BGCs to their secondary metabolites. GCFs have been used as functional markers in human health studies and to study the ability of soil to suppress fungal pathogens. Given their direct relationship to catalytic enzymes, and compounds produced from their encoded pathways, BGCs/GCFs can serve as a proxy to explore the chemical space of microbial secondary metabolism. Cataloging GCFs in sequenced microbial genomes yields an overview of the existing chemical diversity and offers insights into future priorities. Tools such as BiG-SLiCE and BIG-MAP have emerged with the sole purpose of unveiling the importance of BGCs in natural environments.


BiG-SLiCE

BiG-SLiCE (Biosynthetic Genes Super-Linear Clustering Engine), is an automated pipeline tool designed to cluster massive numbers of BGCs. By representing them in
euclidean space Euclidean space is the fundamental space of geometry, intended to represent physical space. Originally, that is, in Euclid's Elements, Euclid's ''Elements'', it was the three-dimensional space of Euclidean geometry, but in modern mathematics ther ...
, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. from genomic and metagenomic data of diverse organisms. The BiG-SLiCE workflow starts at
vectorization Vectorization may refer to: Computing * Array programming, a style of computer programming where operations are applied to whole arrays instead of individual elements * Automatic vectorization, a compiler optimization that transforms loops to vec ...
(
feature extraction In machine learning, pattern recognition, and image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning a ...
), converting input BGCs provided from dataset of cluster GenBank files from antiSMASH and MIBiG into vectors of numerical features based on the absence/presence and bitscores of hits obtained from querying BGC gene sequences from a library curated of profile
Hidden Markov Model A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it X — with unobservable ("''hidden''") states. As part of the definition, HMM requires that there be an ob ...
(pHMMs) of biosynthetic domains of BGCs. Those features are then processed by a super-linear clustering algorithm based on
BIRCH A birch is a thin-leaved deciduous hardwood tree of the genus ''Betula'' (), in the family Betulaceae, which also includes alders, hazels, and hornbeams. It is closely related to the beech-oak family Fagaceae. The genus ''Betula'' contains 30 ...
clustering, resulting in
centroid In mathematics and physics, the centroid, also known as geometric center or center of figure, of a plane figure or solid figure is the arithmetic mean position of all the points in the surface of the figure. The same definition extends to any ob ...
feature vectors representing the GCF models. All BGCs in the dataset are queried back against those models, outputting a list of GCF membership values for each BGC. Then a global cluster mapping is done using
k-means ''k''-means clustering is a method of vector quantization, originally from signal processing, that aims to partition ''n'' observations into ''k'' clusters in which each observation belongs to the cluster with the nearest mean (cluster centers o ...
to group all GCF centroid features in GCF bins. After that another round of membership assignment is performed to match the full set of BGC features into the resulting GCF bins. In the end, it produces archives, which then can be used to perform further analysis (via external scripts) or to visualize the result in a user-interactive application. Satria et al. demonstrated the utility of such analyses by reconstructing a global map of secondary metabolic diversity across
taxonomy Taxonomy is the practice and science of categorization or classification. A taxonomy (or taxonomical classification) is a scheme of classification, especially a hierarchical classification, in which things are organized into groups or types. ...
to identify the uncharted biosynthetic potential of 1.2 million biosynthetic gene clusters. This opens up new possibilities to accelerate
natural product A natural product is a natural compound or substance produced by a living organism—that is, found in nature. In the broadest sense, natural products include any substance produced by life. Natural products can also be prepared by chemical syn ...
discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied
taxa In biology, a taxon (back-formation from ''taxonomy''; plural taxa) is a group of one or more populations of an organism or organisms seen by taxonomists to form a unit. Although neither is required, a taxon is usually known by a particular nam ...
, more information can be mined to highlight their potentially novel chemistry.


BiG-MAP

Since BGCs are an important source of metabolite production, current tools for identifying BGCs focus their efforts on mining genomes to identify their genomic landscape, neglecting relevant information about their abundance and expression levels which in fact, play an important ecological role in triggering phenotype dependent-metabolite concentration. That is why in 2020 BiG-MAP (Biosynthetic Gene cluster Meta’omics Abundance Profiler), an automated pipeline that helps to determine the abundance (metagenomic data) and expression (metatranscriptomic data) of BGCs across microbial communities. It shotguns sequencing reads to gene clusters that have been predicted by antiSMASH or gutSMASH. BiG-MAP splits its workflow in four main modules. * BiG-MAP.family: redundancy filtering on the gene cluster collection in order to reduce computing time and avoid ambiguous mapping. Using a
MinHash In computer science and data mining, MinHash (or the min-wise independent permutations locality sensitive hashing scheme) is a technique for quickly estimating how Similarity measure, similar two sets are. The scheme was invented by , and initially ...
-based algorithm, MASH, BiG-MAP estimates distance among protein sequences which then is used to select a representative gene cluster with the aid of
k-medoids The -medoids problem is a clustering problem similar to -means. The name was coined by Leonard Kaufman and Peter J. Rousseeuw with their PAM algorithm. Both the -means and -medoids algorithms are partitional (breaking the dataset up into group ...
clustering. Finally, the selected gene clusters are clustered into GCFs using BiG-SCAPE, considering account architectural similarity, thus relating more distantly related gene clusters which produce the same chemical product in different organisms * BiG-MAP.download: optional module that uses a list of Sequence Read Archive (SRA) database; * BiG-MAP.map: reads the set of representative GCFs obtained from the first module. Maps reads to GCFs separately and, it reports combined abundance or expression levels per family. Reads are mapped to the representative of the GCFs using the short-read aligner Bowtie2, which are then converted into Reads Per Kilobase Million (RPKM) to be averaged over the GCFs size * BiG-MAP.analyse: profiles abundance. RPKM values are normalized using Cumulative Sum Scaling (CSS) to account for sparsity. Differential expressions analyses use zero-inflated Gaussian distribution mixture models (ZIG-models) or Kruskal-Wallis. The pipeline displays the results s plots that show gene cluster abundance/expression (heatmaps), log fold change (bar plot), coverage values, and housekeeping gene expression values for metatranscriptomic data (heatmap).


Decodification of RiPPs chemical structures

The increase of experimentally characterized
ribosomally synthesized and post-translationally modified peptides Ribosomally synthesized and post-translationally modified peptides (RiPPs), also known as ribosomal natural products, are a diverse class of natural products of ribosomal origin. Consisting of more than 20 sub-classes, RiPPs are produced by a var ...
(RiPPs), together with the availability of information on their sequence and chemical structure, selected from databases such as BAGEL, BACTIBASE, MIBIG, and THIOBASE, provide the opportunity to develop machine learning tools to decode the chemical structure and classify them. In 2017, researchers at the National Institute of Immunology of New Delhi, India, developed RiPPMiner software, a bioinformatics resource for decoding RiPP chemical structures by genome mining. The RiPPMiner web server consists of a query interface and the RiPPDB database. RiPPMiner defines 12 subclasses of RiPPs, predicting the cleavage site of the leader peptide and the final cross-link of the RiPP chemical structure.


Identification of RiPPs and prediction of RiPP Class

RiPPs analysis tools such as antiSMASH and RiPP-PRISM use HMM of modifying enzymes present in biosynthetic gene clusters in RiPP to predict the RiPP subclass. Unlike these tools, RiPPMiner uses a
machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
model, trained with 513 RiPPs, that uses the amino acid sequence of the RiPP gene uniquely to identify and subclass them. RiPPMiner differentiates RiPPs from other proteins and peptides using a
support-vector machine In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratorie ...
model trained on 293 experimentally characterized RiPPs as a positive data set, and 8140 genomes encoded non-RiPPs polypeptides as negative data set. The negative data set included SWISSProt entries similar in length to RiPPs, e.g., 30s ribosomal proteins, matrix proteins,
cytochrome B Cytochrome b within both molecular and cell biology, is a protein found in the mitochondria of eukaryotic cells. It functions as part of the electron transport chain and is the main subunit of transmembrane cytochrome bc1 and b6f complexes. F ...
proteins, etc. The support vectors consist of amino acid composition and dipeptide frequencies. Benchmarking on an independent dataset (not included in training) using a two-fold cross-validation approach indicated sensitivity, specificity, precision and MCC values of 0.93, 0.90, 0.90, and 0.85, respectively. This indicates the model's predictive power for distinguishing between RiPPs and non-RiPPs. For prediction of RiPP class or sub-class, a Multi-Class SVM was trained using the amino acid composition and dipeptide frequencies as feature vectors. During the training of the Multi-Class SVM, available RiPP precursor sequences belonging to a given class (e.g. lasso peptide) were used as a positive set, while RiPPs belonging to all other classes were used as negative set.


Prediction of cleavage site

Out of the four major RiPP classes that had more than 50 experimentally characterized RiPPs in RiPPDB, SVM models for prediction of cleavage sites were developed for lanthipeptides, cyanobactins, and lasso peptides. In order to develop SVM for prediction of cleavage site for lanthipeptides, 12 mer peptide sequences centered on the cleavage sites were extracted from a set of 115 lanthipeptide precursor sequences with known cleavage pattern. This resulted in a positive dataset of 103 unique 12 mer peptides harboring the cleavage site at the center, while the other 12 constituted the negative dataset. Feature vectors for each of these mers consisted of the concatenation of 20-dimensional vectors corresponding to each of the 20 amino acids. An SVM model for prediction of cleavage site was developed and benchmarked using 2-fold cross-validation, where half of the data were used in training and the other half in testing. SVM models were developed for the prediction of the cleavage sites in cyanobactin and lasso peptides. Based on analysis of the ROC curves, a suitable score cutoff was chosen for the prediction of cleavage sites in lanthipeptides and lasso peptides.


Prediction of cross-links

The algorithm for the prediction of cross-links and deciphering the complete chemical structure of RiPP has been implemented for lanthipeptides, lasso peptides, cyanobactins, and thiopeptides. The prediction of lanthionine linkages in lanthipeptides was carried out using machine learning. A dataset of 93 lanthipeptides whose chemical structures was known was taken from RiPPDB. For each lanthipeptide in this set, the sequence of the core peptide was scanned for strings or sub-sequences of the type Ser/Thr-(X)n-Cys or Cys-(X)n-Ser/Thr to enumerate all theoretically possible cyclization patterns. Out of these sequence strings, the strings corresponding to Ser/Thr-Cys or Cys-Ser/Thr pairs which were linked by lanthionine bridges were included in the positive set, while all other strings were included in the negative set.


Mass spectral similarity scoring

Many tandem mass spectrometry (MS/MS) based metabolomics studies, such as library matching and molecular networking, use spectral similarity as a proxy for structural similarity. Spec2vec algorithm provides a new way of spectral similarity score, based on
Word2Vec Word2vec is a technique for natural language processing (NLP) published in 2013. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or ...
. Spec2Vec learns fragmental relationships within a large set of spectral data, in order to assess spectral similarities between molecules and to classify unknown molecules through these comparisons. For systemic annotation, some metabolomics studies rely on fitting measured fragmentation mass spectra to library spectra or contrasting spectra via network analysis. Scoring functions are used to determine the similarity between pairs of fragment spectra as part of these processes. So far, no research has suggested scores that are significantly different from the commonly utilized cosine-based similarity.


Databases

An important part of bioinformatics is the management of big datasets, known as databases of reference. Databases exist for each type of biological data, for example for biosynthetic gene clusters and metagenomes.


General databases by bioinformatics


National Center for Biotechnology Information

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the
GenBank The GenBank sequence database is an open access, annotated collection of all publicly available nucleotide sequences and their protein translations. It is produced and maintained by the National Center for Biotechnology Information (NCBI; a part ...
nucleic acid sequence database and the
PubMed PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The United States National Library of Medicine (NLM) at the National Institutes of Health maintain the ...
database of citations and abstracts for published life science journals. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. Resources include PubMed Data Management, RefSeq Functional Elements, genome data download, variation services API, Magic-BLAST, QuickBLASTp, and Identical Protein Groups. All of these resources can be accessed through NCBI.


Bioinformatics analysis for biosynthetic gene clusters


antiSMASH

antiSMASH allows the rapid genome-wide identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genomes. It integrates and cross-links with a large number of in silico
secondary metabolite Secondary metabolites, also called specialised metabolites, toxins, secondary products, or natural products, are organic compounds produced by any lifeform, e.g. bacteria, fungi, animals, or plants, which are not directly involved in the norm ...
analysis tools.


gutSMASH

gutSMASH is a tool that systematically evaluates bacterial metabolic potential by predicting both known and novel
anaerobic Anaerobic means "living, active, occurring, or existing in the absence of free oxygen", as opposed to aerobic which means "living, active, or occurring only in the presence of oxygen." Anaerobic may also refer to: * Anaerobic adhesive, a bonding a ...
metabolic gene clusters (MGCs) from the gut
microbiome A microbiome () is the community of microorganisms that can usually be found living together in any given habitat. It was defined more precisely in 1988 by Whipps ''et al.'' as "a characteristic microbial community occupying a reasonably well ...
.


MIBiG

MIBiG, the minimum information about a biosynthetic gene cluster specification, provides a standard for annotations and
metadata Metadata is "data that provides information about other data", but not the content of the data, such as the text of a message or the image itself. There are many distinct types of metadata, including: * Descriptive metadata – the descriptive ...
on biosynthetic gene clusters and their molecular products. MIBiG is a Genomic Standards Consortium project that builds on the minimum information about any sequence (MIxS) framework. MIBiG facilitates the standardized deposition and retrieval of biosynthetic gene cluster data as well as the development of comprehensive comparative analysis tools. It empowers next-generation research on the biosynthesis, chemistry and ecology of broad classes of societally relevant bioactive
secondary metabolites Secondary metabolites, also called specialised metabolites, toxins, secondary products, or natural products, are organic compounds produced by any lifeform, e.g. bacteria, fungi, animals, or plants, which are not directly involved in the nor ...
, guided by robust experimental evidence and rich metadata components.


SILVA

SILVA is an interdisciplinary project among biologists and computers scientists assembling a complete database of RNA ribosomal (rRNA) sequences of genes, both small ( 16S, 18S, SSU) and large ( 23S,
28S 28S ribosomal RNA is the structural ribosomal RNA (rRNA) for the LSU rRNA, large subunit (LSU) of eukaryotic cytoplasmic ribosomes, and thus one of the basic components of all eukaryotic cells. It has a size of 25S in plants and 28S in mammals, ...
, LSU) subunits, which belong to the bacteria, archaea and eukarya domains. These data are freely available for academic and commercial use.


Greengenes

Greengenes is a full-length
16S rRNA 16S rRNA may refer to: * 16S ribosomal RNA 16 S ribosomal RNA (or 16 S rRNA) is the RNA component of the 30S subunit of a prokaryotic ribosome ( SSU rRNA). It binds to the Shine-Dalgarno sequence and provides most of the SSU structure. The g ...
gene database that provides chimera screening, standard alignment and a curated taxonomy based on de novo tree inference. Overview: * 1,012,863 RNA sequences from 92,684 organisms contributed to RNAcentral. * The shortest sequence has 1,253 nucleotides, the longest 2,368. * The average length is 1,402 nucleotides. * Database version: 13.5.


Open Tree of Life Taxonomy

Open Tree of Life Taxonomy (OTT) aims to build a complete, dynamic, and digitally available Tree of Life by synthesizing published phylogenetic trees along with taxonomic data. Phylogenetic trees have been classified, aligned, and merged. Taxonomies have been used to fill in sparse regions and gaps left by phylogenies. OTT is a base that has been little used for sequencing analyzes of the 16S region, however, it has a greater number of sequences classified taxonomically down to the genus level compared to SILVA and Greengenes. However, in terms of classification at the edge level, it contains a lesser amount of information


Ribosomal Database Project

Ribosomal Database Project (RDP) is a database that provides RNA ribosomal (rRNA) sequences of small subunits of domain bacterial and archaeal ( 16S); and fungal rRNA sequences of large subunits (
28S 28S ribosomal RNA is the structural ribosomal RNA (rRNA) for the LSU rRNA, large subunit (LSU) of eukaryotic cytoplasmic ribosomes, and thus one of the basic components of all eukaryotic cells. It has a size of 25S in plants and 28S in mammals, ...
).


References

__FORCETOC__ {{Differentiable computing Machine learning Bioinformatics