dataesr/scientific-paragraphs-categorization huggingface dataset:
Bold values show significant p-value at α = 0. 05 pairwise G ST , G" ST , D ST and Φ ST among populations using a combined Fisher's p-value among loci corrected for a multiple testing using Holm's formula. Clean_Dataset: using a brute (br) dataset coming from pyRAD or a cleaned (cl) dataset removing loci with a Minimum Allele Frequency (MAF) below 5%, with an observed heterozygosity above 0. 5 and detected under selection by Bayescan. Th: Lake Thingvallavatn, Sv: Lake Svartárvatn, Kl: Klapparós, Sa: Sandur. Figure  S5. 1: Global and pairwise differentiation values of each RADseq locus among the four populations of Crangonyx islandicus. D ST , G ST and G" ST are computed using haplotype information of a dataset considering indels as missing data and a minimal coverage of the RADSeq loci of 3 (C3Hmiss dataset).
The Φ STs were computed using the sequences of the same dataset. The dataset C3Hmiss before (a) (n = 103), and after (b) removing the loci in low frequency, considered as potential paralogs or under selection (n = 71). Th: Lake Thingvallavatn, Sv: Lake Svartárvatn, Kl: Klapparós, Sa: Sandur. The black bands display the median distance, the box represents the interquartile distance, the whiskers are long up to 1. 5 the interquartile distance while the black dots represent the outliers. Figure  S5. 2: Global and pairwise differentiation values of each RADseq locus among the four populations of Crangonyx islandicus. D ST , G ST and G" ST are computed using haplotype information of a dataset considering indels as missing data and a minimal coverage of the RADSeq loci of 2 (Cov2Hmiss dataset). The Φ STs were computed using the sequences of the same dataset.
The dataset Cov2Hmiss before (a) (n = 295), and after (b) removing the loci in low frequency, considered as potential paralogs or under selection (n = 202). Th: Lake Thingvallavatn, Sv: Lake Svartárvatn, Kl: Klapparós, Sa: Sandur. The black bands display the median distance, the box represents the interquartile distance, the whiskers are long up to 1. 5 interquartile distance while the black dots represent the outliers. Figure  S5. 3: Phylogenetic tree of 73 individuals of Crangonyx islandicus based on 620 bp of the partial mtDNA NADH dehydrogenase subunit 5 gene detected as putatively under selection by Bayescan. This maximum likelihood tree was built with Phyml  [98]  using a GTR evolution model. Tips represent the different haplotypes and tip labels show the label of the site location as described in Table  S4.
1 and the number of individuals (i. e. , frequencies) belonging to a given haplotype is provided within brackets. The branch support (aLRT) displayed at nodes was obtained with the difference in likelihoods with and without the respective branch. Geographic origin is presented with vertical bars using the following letters: SV: southwestern Iceland, S: southern Iceland, SA: southeastern Iceland and N: northern Iceland. Figure  S5. 5: (a) Maximum clade credibility tree (MCCT) and (b) posterior distribution of theta (θ = Ne*µ) parameters inferred by SNAPP  [74]  according to the Bayes Factor Delineation's (BFD*  [76] ) best scenario for the four populations of Crangonyx islandicus using 223 unlinked bi-allelic SNP dataset (C2SNPr). Node number on the MCCT displays the posterior probability of the node and the dark blue node bar presents the 95% HPD interval of the node age.
Data Availability Statement: Publicly available datasets were analyzed in this study. The data and parameter files, as well as R scripts and bash codes used to perform the analyses of the mitochondrial and the RADseq data, are available at: the Zenodo Digital Repository https://doi. org/10. 5281/ zenodo. 7506345. The RADseq data are available in NCBI with the BioProject number PRJNA917071. The new CO1, 16S, NADH5 and H3 DNA sequences have been deposited in GenBank and accession numbers are available in Supplementary Materials Tables S1. 1 and S4. 1.
Nowadays, generating omics data is a common activity for laboratories in biology. Experimental protocols to prepare biological samples are well described, and technical platforms to generate omics data from these samples are available in most research institutes. Furthermore, manufacturers constantly propose technical improvements, simultaneously decreasing the cost of experiments and increasing the amount of omics data obtained in a single experiment. In this context, biologists are facing the challenge of dealing with large omics datasets, also called "big data" or "data deluge". Working with omics data raises issues usually handled by computer scientists and thus cooperation between biologists and computer scientists has become essential to efficiently study cellular mechanisms in their entirety, as omics data promise.
Introduction There are different types of omics data, each revealing an aspect of cell complexity. To illustrate this complexity, we propose in Figure  1  an analogy between the functions of a cell and that of a factory. The different omics data types are replaced there, in their specific context. Cells are the building blocks of living organisms. They can be pictured as microscopic, automated factories, made up of thousands of biological molecules (or molecular components) that work together to perform specific functions. Basically, there are four main types of molecular components: DNA, RNA, proteins and metabolites. The whole population of one type of cellular component is named with the suffix -ome, i. e. genome (DNA), transcriptome (RNA), proteome (proteins) and metabolome (metabolites) (Figure  1 ).
The scientific fields, which aim at studying those respective populations, are named with the suffix -omics, i. e. genomics, transcriptomics, proteomics and metabolomics. The common point between the different type of omics data is that they all arise from high throughput experimental strategies that allow the simultaneous observation of all individual components that constitute either the genome, the transcriptome, the proteome or the metabolome (1). The genome is made of DNA molecules, which are the carrier of genetic information. It can be imagined as the blueprint library of the cell (Figure  1 ). From a chemical point of view, DNA molecules are polymers (or sequences) of simpler chemical units called nucleotides. There are four main types of nucleotides: Adenine (A), Thymine (T), Cytosine (C) and Guanine (G).
DNA molecules are organized into chromosomes, which are compacted in the cell nucleus. The genome is directly connected to the transcriptome and the proteome (see next sections). The information to synthesize RNA molecules (transcriptome) and proteins (proteome) is encoded in specific regions of the DNA sequence called genes (Figure  1 ). Genes are made of successive nucleotides (clustered into codons), which correspond to amino acids, i. e. the molecules that constitute the proteins. The correspondence between nucleotides, codons and amino acids is known as the genetic code. To summarize, a genomics dataset thus contains the sequences of DNA molecules present in a cell (or a population of cells) and can be seen as a copy of the cell's blueprint library (its genome) written as a long sequence of A, T, C and G.
The transcriptome is made of RNA molecules. Multiple types exist and they can be roughly classified into messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and non-coding RNA (ncRNA). Transcriptomics datasets mainly focus on mRNAs, which are the intermediate messengers between the genome and the proteome (see previous paragraph). The transcriptome is thus intimately connected to the genome and the proteome (Figure  1 ). Notably, the RNA polymerase is required to generate mRNA, reading the genome during transcription. In eucaryotes, mRNAs exit the nucleus to be used as templates by ribosomes (a macromolecular complex made of rRNA and proteins), to synthesize proteins by assembling amino acids (following the genetic code) during translation. Compared to the genome, the transcriptome is much more dynamic.
The cell population of mRNA molecule varies according to cell requirement in proteins and a transcriptomic dataset lists all sequences of mRNA present at a given time. They can be seen as snapshots of which parts of the genome are currently transcribed and in which proportion. Following up on the genome analogy presented in Figure  1 , mRNAs can be seen as active copies of the cell's blueprints that are more or less actively used. The proteome is made of proteins, i. e. macromolecules made with one or several polymers of amino acids. Proteins are extraordinarily diverse in their three-dimensional (3D) conformations and associated functions. To illustrate this diversity, some proteins constitute the backbone of the cell structure, others detect or transmit external or internal chemical signals and a large portion of them (enzymes) catalyze chemical reactions of the metabolism (the whole set of chemical reactions sustaining the cell).
Proteins are also responsible for the regulation and expression (transcription and translation) of the genetic information (see previous paragraph). Protein functions are closely linked to their 3D spatial conformation and all processes of the cells are based on protein activities (Figure  1 ). The proteome is as dynamic as the transcriptome because the set of proteins present at a given time in a cell varies accordingly to the current state and function of this cell. Proteomics datasets give a snapshot of which proteins are present at a given moment in the life of the cell. Genomics, transcriptomics and proteomics resume the classical central dogma of biology, as first stated by Francis Crick in 1957. Even if it has been further detailed since, with for instance a better understanding of epigenomics, it still effectively summarizes the principal flow of information between the main molecular components of the cell: DNA is transcribed into RNA which is translated into proteins.
To end this description of omics data types, we believe it is important to mention the metabolome (Figure  1 ). The metabolome is made of metabolites, small molecules that are protein substrates in chemical reactions. Nucleotides and amino acids cited before, are metabolites, as well as other molecules like lipids (forming bi-layers membranes that compartmentalize the cell), or ATP (a molecule used as intracellular energy transfer). To extend, again, the analogy, metabolites can be seen as the raw materials used by the automated microscopic factory (Figure  1 ). Metabolomics datasets peek into the population of metabolites in a cell at a given time. Again, it is important to specify that if each cited "omics" field give an assessment of its associated "ome" population, it is a quite "blurred" one.
Everything is intertwined in a cell. Moreover, most omics studies give only an average observation on a population of cells. Multi-omics and single cells techniques are trying to overcome these limitations. In this chapter, we detail the different types of files used for omics data and present examples of databases where they are stored. We introduce different methods for generating omics data and finally provide some applications of omics data in fundamental research, cancer research and pandemic response. To describe the files used to store omics information, it is necessary to consider genomics and transcriptomics on one side and proteomics and metabolomics on the other side. Indeed, these files are generated by different experimental techniques, which are respectively sequencing (for genomics and transcriptomics) and mass spectrometry (for proteomics and metabolomics) (Figure  2 ).
For each group, two types of files must be distinguished: the ones that are directly obtained after the applications of experimental protocols, i. e. the raw omics data files, and the ones that are generated by downstream informatic analyses, i. e. the processed omics data files (Figure  2 ). Experimental protocols and the informatic treatments applied to raw data files will be detailed in the next section. Genomics and transcriptomics raw data files are essentially nucleotide sequence files. In that respect, the FASTA and the FASTQ text formats are commonly used. FASTA was created by Lipman and Pearson in 1985 as an input for their software (2) and became a de facto standard, without any clear statement acknowledging it (3). This probably explains the absence of a common file extension (e.
g. fasta,. fna,. faa) even if FASTA is a unified file type. FASTA files contains one or several sequences. A sequence begins with a description line starting with the character ">". NCBI databases (see next sections) have unified rules to write this line  1. Subsequent lines contain the sequence itself splitted into multiples blocks of 60 to 80 characters (one per line). With nucleic acid sequences, the sequence lines are a series of A/T/C/G/U characters, representing the nucleic acids: adenine, thymine, cytosine, guanine, and uracil (the latter replacing thymine in RNA). FASTQ is the file format for the raw data generated by the sequencer in genomics and transcriptomics (Figure  2 ). The first two lines are similar as with FASTA file: identification line starts with "@" instead of ">" and the second line contains the nucleic sequence, but a quality score is associated with each position of the sequence (i.
e. each letter in the sequence line). This score is called "Phred score", and it codes the probability of error in the identification of this nucleotide (3). It goes from 0 to 62 and is coded in ASCII symbols. This allows to code any score using a single symbol, keeping the same length as the sequence line. FASTA and FASTQ files can be opened with any text editor software. FASTQ files are mainly lists of short sequences called "reads" (between 50 to 200 nucleic acids), which need to be processed (aligned or assembled) to be further analyzed. Alignment data files are one type of processed data. Indeed, reads in FASTQ files can be aligned to a reference genome sequence to allow further analyses (see below for pipeline description and example of applications). The text file format used in this case is the SAM 2 (sequence alignment and mapping) format (4, 5).
It can be further compacted into its binary equivalent, which are BAM or CRAM formats (6). The file formats for proteomics and metabolomics data are not as homogeneous as for genomics and transcriptomics. At least seventeen types of formats exist for mass spectrometry files (see below) (7). Each machine manufacturer created its own, adapted to proprietary software to read and analyze it, thus multiplying formats. In an effort to facilitate data exchange and to avoid data loss (in case of no more readable old file formats), HUPO (8) and PSI  3  created the open-source mzML 4 format (XML text file with specific tag syntax) in 2011 (9). In the main databases that host mass spectrometry results files, most of the files are in the RAW format, developed by Thermo Scientific. These binary files contain retention time, intensity, and mass-to-charge ratios (see later sections).
Software like Peaks, Mascot, MaxQuant or Progenesis (10, 11) use these files to identify proteins present in the sample and to quantify them. Results from these analyses are shared through two other text file formats: mzIdentML 5 and mzTab  6. 1 Note that many other file formats exist. One of the most critical for omics data analyses concerns the annotations of features on a DNA, RNA or protein sequence. They are shared through the General Feature Format (GFF  7  ) that is a text file with nine tabulated separated fields: sequence, source of the annotation, feature, start of the feature on the sequence, end of the feature, score, strand, phase and attributes.
… and shared through multiple public databases The set of public biological databases hosting omics data is large and constantly evolving. Omics terminology started being regularly used in the 2000s. Between 1991 and 2016 (25 years) more than 1,500 "molecular biology" databases were presented in publications, with a proliferation rate of more than 100 new databases each year (12). These numbers are only the visible part of existing databases. How many have been created without being published? Around 500 of those databases are roughly co-occurrent with the apparition of the World Wide Web, the very internet application allowing the creation of online databases. The availability of molecular biology databases decreased by only 3. 8% per year from 2001 to 2016 (12). This shows a sustained motivation from the community to create and maintain public platforms to share data.
But it also highlights that this motivation comes more from a shared need for easy access to data rather than a supervised effort to coordinate approaches and unify sources. Such efforts indeed exist, for example the ELIXIR project started in 2013 as an effort to unify all European centers and core bioinformatics resources into a single, coordinated infrastructure (13   10. Consistent efforts are made to cross reference biological components (genes, proteins, metabolites) through the diversity of databases. Each database represents terabytes and petabytes of biological information (43 000 Terabytes of sequences data just for SRA  11  ), and the scale of the network they form though cross reference is hard to conceptualize. This is the "big data'' in biology and even more are generated every day.
Transcriptomics is the more advanced single cell omics but single cell genomics is also used in SNP and copy number variation screening (see section 3-2). Proteomics and metabolomics data are still challenging to obtain at a single cell level: one cell yields only 250-300 pg (46) of proteins when MS in-depth measurement still necessitates population scale yield. But thanks to innovations in sample preparation and experimental design, single cell proteomics assessments scaled up from a few hundred to more than a thousand identified proteins in just four years (47). 10 Machine Learning for Brain Disorders, Chapter 10 4. Which applications for omics data?.
In fundamental research Describing biological systems implies to identify, quantify, and functionally connect their individual molecular components. Given the diversity of cellular components and their multiple interlocking functions, the large scale of omics data empowers the characterization of biological systems. As stated before, each type of "omics" is an assessment of a specific subpopulation of molecular components. Mining omics data thus allows bulk identification of the nature (sequence and structure), location, function and abundance of molecular components in those subpopulations. Genomics data are making the genome sequences of thousands of species accessible. The first direct application of these resources is the annotation of genomic features onto those genomic sequences. Protein-coding genes, tRNA and rRNA genes, pseudogenes, transposons, single nucleotide polymorphisms, repeated regions, telomeres, centromeres… Genomic features are numerous, and DNA sequences alone can be enough to recognize patterns specific to some of them.
Proteomics data allows the identification and quantification of proteome. Proteome does not totally correlate with transcriptome. RNA can be spliced (assembly of the mRNA from exons, not always the same and in the same order) and proteins undergo several posttranslational modifications (minor changes in the chemical structure of the protein) and relocalization (53). Cellular pathways and phenotypes thus cannot be fully understood only through transcriptomics assessments. Proteomics completes the information given by genomics and transcriptomics by describing the central dogma third -ome (Figure  1 ). Multi-omics analysis, taking advantage of several omics insights in the same experimental approach, comes with several challenges. Generating several types of omics data comes with a significant investment in time, skilled manpower and money (1).
In medical research An early application of genomics in medical research is the Genome-Wide Association Studies (GWAS). By comparing genome sequences from a large population of individuals (both healthy and sick), GWAS highlight SNPs (single-nucleotide polymorphism) significantly more frequent in individuals with the disease. Correlation does not mean causality, but GWAS can give a first clue of the metabolic pathways or cellular components involved in the disease (55). This strategy has proven to be efficient in the case of "common complex diseases". Unlike Mendelian diseases (which are rarer), the heritability (genetic origin) of these diseases depends on hundreds of SNPs with small effect sizes, which GWAS studies help identify (56 Getting omics data in brain tissues studies is promising but challenging because of brain specificity.
Conclusion Genomics, transcriptomics, proteomics and metabolomics are arguably the most developed and used omics, but they are not the only ones. Other omics describe other sides of the functioning of the cell, which require intricate relationships between omics levels. For example, epigenomics describes the transitory chemical modifications of DNA, lipodomics looks at the lipidic sub-population of metabolites (Figure  1 ). Omics diversity mirrors the complexity of cell systems. With the constant improvement of measurement techniques, possibilities to assess ever larger subsystems of the cells are increasing. Omics datasets generation is paired with the development of software, essential tools to generate, read, and analyze them. By design, computer science is therefore omnipresent in modern "big data" biology.
is a reference database for mass spectrometry-based proteomics data. Raw files containing spectra are available with associated identification and quantification information. For metabolomic data, MetaboLights (19) is an archive data repository and a knowledge database. It lists metabolite structures, functions, locations alongside reference raw spectra. Those databases are generalist references, and many more specialized databases exist: 89 new databases are reported in the 2021 NAR database issue and a dozen of them are omics specific (20). For example, AtMAD is a repository for large-scale measurements of associations between omics in Arabidopsis thaliana and Aging Atlas gathers aging related multi-omics data(21, 22). Finally, noteworthy is the existence of general-purpose open repositories like Zenodo 9 , which allow researchers to deposit articles, research datasets, source codes, and any other research-related digital information.
Researchers thus receive credit by making their work more easily findable and reusable, and hence support the application of the FAIR (findable, accessible, interoperable, reusable) data principles They organize genome sequences together with annotations and include sequence comparison and visual exploration tools. Transcriptomics data can be deposited into several databases, like Gene Expression Omnibus (GEO) (16) initially dedicated to microarrays datasets, which is structured into samples forming datasets. Tools are available to query and download gene 3 HUPO Proteomics Standards Initiative 4 mzML 1. 1. 0 Specification | HUPO Proteomics Standards Initiative 5 mzIdentML | HUPO Proteomics Standards Initiative 6 mzTab Specifications | HUPO Proteomics Standards Initiative 7 GFF/GTF File Format 8 NCBI ).
This notably produces the ELIXIR Core Data Resources (created in 2017), a set of selected European databases, meeting defined requirements and the website "bio. tools", i. e. a comprehensive registry of available software programs and bioinformatics tools. The United States National Center for Biotechnology Information (NCBI 8 ) databases are also main references. Given the "raw" nature of omics dataset, they are stored in archive data repositories: raw data from scientific articles, shared on databases easily accessible for reproducibility. Except for the Sequence Read Archive (SRA), the databases cited here are mixed ones: they host raw archive data and knowledge extracted from them. For genomics dataset, NCBI database Genome (14) and EMBL-EBI (member of ELIXIR) database Ensembl (15) are references.