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Bioinformatics Market Size 2025-2029

The bioinformatics market size is forecast to increase by USD 15.98 billion, at a CAGR of 17.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the reduction in the cost of genetic sequencing and the development of advanced bioinformatics tools for Next-Generation Sequencing (NGS) technologies. These advancements enable faster and more accurate analysis of genomic data, leading to new discoveries and applications in various industries, including healthcare, agriculture, and research. However, the market faces a major challenge: the shortage of trained laboratory professionals capable of handling and interpreting the vast amounts of data generated by these technologies.
This skills gap poses a significant obstacle to the full realization of the potential of bioinformatics, necessitating strategic investments in workforce development and training programs. Companies seeking to capitalize on the opportunities presented by the market must address this challenge while continuing to innovate and develop sophisticated tools to meet the evolving needs of their customers.

What will be the Size of the Bioinformatics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in genome editing, data mining, and sequence alignment, among other techniques. Molecular modeling and high-throughput screening are essential tools in drug discovery, while bioimage analysis and gene ontology play a pivotal role in systems biology. Metabolomics data and phylogenetic analysis contribute to a deeper understanding of biological processes.

Machine learning and artificial intelligence are increasingly being integrated into bioinformatics pipelines, enabling more accurate predictions of protein structures, disease modeling, and biomarker discovery. Next-generation sequencing and transcriptomics profiling have revolutionized genomic variation studies, leading to a wealth of new insights. For instance, a recent study employing single-cell sequencing and deep learning identified over 60 distinct cell types in the human brain, expanding our knowledge of neurobiology.

The market is expected to grow at a robust rate, with industry experts projecting a 15% annual increase in demand for these advanced analytical solutions. Genome sequencing, epigenomics studies, RNA interference, and proteomics analysis are just a few more applications of bioinformatics, continually pushing the boundaries of scientific discovery. From gene expression to network analysis, the potential applications of bioinformatics are vast and ever-expanding.

How is this Bioinformatics Industry segmented?

The bioinformatics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Molecular phylogenetics
  Transcriptomic
  Proteomics
  Metabolomics


Product

  Platforms
  Tools
  Services


End-user

  Pharmaceutical and biotechnology companies
  CROs and research institutes
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Application Insights

The molecular phylogenetics segment is estimated to witness significant growth during the forecast period.

In the dynamic and innovative realm of bioinformatics, various techniques and tools are propelling research forward. Molecular phylogenetics, a branch of bioinformatics, is a prime example of this progress. This technique, which uses molecular data to explore evolutionary relationships among species, has significantly advanced our understanding of living organisms in fields such as drug discovery, disease diagnosis, and conservation biology.

For instance, molecular phylogenetics plays a pivotal role in studying viral evolution. By analyzing the molecular data of distinct virus strains, researchers can trace their evolution and uncover their origins and transmission patterns. Furthermore, industry growth in bioinformatics is anticipated to expand by approximately 15% annually, as per recent estimates, underscoring the market's continuous evolution and impact.

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The Molecular phylogenetics segment was valued at USD 4.48 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 43% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the for

Heat maps showing branch specific ΔlnL4 scores of genes in pathways that score significant (q < 0.2) in the gene set enrichment test after removing overlap between sets ('pruning'). The ΔlnL4 score is computed as the fourth root of the log-likelihood ratio in the branch site test for positive selection. Branches where a pathway scores significant are marked with a '*'. The genes are grouped by hierarchical clustering to visualize blocks with similar signals among branches. Genes for which ΔlnL4 scores were not available (NA) in a certain branch are depicted in grey. Genes are merged (horizontally) with their paralog(s) into an 'ancestral gene' in the branches preceding a duplication and their scores were included only once in the calculation of the SUMSTAT score for these branches. Genes with (vertically) merged branches represent cases where the sequence of one or more species is missing or excluded, resulting in a single 'average' ΔlnL4 score over multiple branches. We used this score when testing each branch separately.

Best tree from partitioned RaxML analysis of concatinated alignment of amino acid sequences for 208 single-copy genes from 35 Agaricales taxa plus four outgroups (Newick format). Includes: -Best-scoring ML tree with branch lengths (RAxML_bestTree.AllConcatinatedProt.phy_partitioned) -Best-scoring ML tree with support values (RAxML_bipartitions.AllConcatinatedProt.phy_partitioned) -Best-scoring ML tree with branch lengths and support values as branch labels (RAxML_bipartitionsBranchLabels.AllConcatinatedProt.phy_partitioned) -Best-scoring ML trees from each bootstrap replicate (RAxML_bootstrap.AllConcatinatedProt.phy_partitioned) -Info about analysis (RAxML_info.AllConcatinatedProt.phy_partitioned)

Global Bioinformatics market size was USD 12.76 Billion in 2022 and it is forecasted to reach USD 29.32 Billion by 2030. Bioinformatics Industry's Compound Annual Growth Rate will be 10.4% from 2023 to 2030. What are the driving factors for the Bioinformatics market?

The primary factors propelling the global bioinformatics industry are advances in genomics, rising demand for protein sequencing, and rising public-private sector investment in bioinformatics. Large volumes of data are being produced by the expanding use of next-generation sequencing (NGS) and other genomic technologies; these data must be analyzed using advanced bioinformatics tools. Furthermore, the global bioinformatics industry may benefit from the development of emerging advanced technologies. However, the bioinformatics discipline contains intricate algorithms and massive amounts of data, which can be difficult for researchers and demand a lot of processing power. What is Bioinformatics?

Bioinformatics is related to genetics and genomics, which involves the use of computer technology to store, collect, analyze, and disseminate biological information, and data, such as DNA and amino acid sequences or annotations about these sequences. Researchers and medical professionals use databases that organize and index this biological data to better understand health and disease, and in some circumstances, as a component of patient care. Through the creation of software and algorithms, bioinformatics is primarily used to extract knowledge from biological data. Bioinformatics is frequently used in the analysis of genomics, proteomics, 3D protein structure modeling, image analysis, drug creation, and many other fields.

Supplementary Microsoft Excel files containing extended data that is referenced in the text.These include:File 1: Select proteins’ 30MY ERC lists, contains multiple-test corrected p-values.

File 2: Pairwise ρ and unadjusted p-value 30MY ERC matrices for all proteins.

File 3: Enrichment results for select top ERC protein sets.

File 4: Zip file containing the mammalian time-scaled phylogeny and maximum likelihood protein trees in newick format.

File 5: Table depicting the total number of taxa present for each protein’s sequence data, along with the number of taxa for which there are paralogy in the uncorrected and 30MY corrected data.

File 6: Branch time to terminal branch rate correlation results for the protein set.

File 7: Chi-squared test results for all proteins testing for whether there is an overrepresentation of rates below the regression line for short branches (

The global Bioinformatics Data Analysis Service market is estimated to be valued at USD XXX million in 2025 and is projected to grow at a compound annual growth rate (CAGR) of XX% during the forecast period from 2025 to 2033. The market growth is attributed to the increasing adoption of bioinformatics in various research fields, such as genomics, transcriptomics, and proteomics. The availability of large-scale genomic and transcriptomic data has led to the development of sophisticated bioinformatics tools and techniques for data analysis, interpretation, and visualization. Furthermore, the growing awareness of personalized medicine and the need for precision medicine are driving the demand for bioinformatics data analysis services. Key market trends include the increasing adoption of cloud-based platforms for bioinformatics analysis, the development of artificial intelligence (AI) and machine learning (ML) algorithms for data analysis, and the emergence of new bioinformatics software and tools. These trends are expected to continue to drive the growth of the Bioinformatics Data Analysis Service market in the coming years. Major players in the market include Illumina, Thermo Fisher Scientific, QIAGEN, Seven Bridges, DNAnexus, SOPHiA GENETICS, Geneious, Macrogen, BGI Genomics, and Biomatters, among others. These companies offer a wide range of bioinformatics data analysis services, including data management, analysis, interpretation, and visualization. The market is expected to be highly competitive in the coming years, with major players focusing on innovation and strategic partnerships to gain market share.

Bioinformatics Software Market Outlook

The global bioinformatics software market size was valued at approximately USD 10 billion in 2023, and it is projected to reach around USD 25 billion by 2032, growing at a robust CAGR of 11% during the forecast period. This remarkable growth is fueled by the increased application of bioinformatics in drug discovery and development, the rising demand for personalized medicine, and the ongoing advancements in sequencing technologies. The convergence of biology and information technology has led to the optimization of biological data management, propelling the market's expansion as it transforms the landscape of biotechnology and pharmaceutical research. The rapid integration of artificial intelligence and machine learning techniques to process complex biological data further accentuates the growth trajectory of this market.

An essential growth factor for the bioinformatics software market is the burgeoning demand for sequencing technologies. The decreasing cost of sequencing has led to a massive increase in the volume of genomic data generated, necessitating advanced software solutions to manage and interpret this data efficiently. This demand is particularly evident in genomics and proteomics, where bioinformatics software plays a critical role in analyzing and visualizing large datasets. Additionally, the adoption of cloud computing in bioinformatics offers scalable resources and cost-effective solutions for data storage and processing, further fueling market growth. The increasing collaboration between research institutions and software companies to develop innovative bioinformatics tools is also contributing positively to market expansion.

Another significant driver is the growth of personalized medicine, which relies heavily on bioinformatics for the analysis of individual genetic information to tailor therapeutic strategies. As healthcare systems worldwide move towards precision medicine, the demand for bioinformatics software that can integrate genetic, phenotypic, and environmental data becomes more pronounced. This trend is not only transforming patient care but also significantly impacting drug development processes, as pharmaceutical companies aim to create more effective and targeted therapies. The strategic partnerships and collaborations between biotech firms and bioinformatics software providers are critical in advancing personalized medicine and enhancing patient outcomes.

The increasing prevalence of complex diseases such as cancer and neurological disorders necessitates comprehensive research efforts, driving the need for robust bioinformatics software. These diseases require multi-omics approaches for better understanding, diagnosis, and treatment, where bioinformatics tools are indispensable. The ongoing research and development activities in this area, supported by government funding and private investments, are fostering innovation in bioinformatics solutions. Furthermore, the development of user-friendly and intuitive software interfaces is expanding the market beyond specialized research labs to include clinical settings and hospitals, broadening the potential user base and enhancing market penetration.

From a regional perspective, North America currently leads the bioinformatics software market, thanks to its advanced technological infrastructure, significant investment in healthcare R&D, and the presence of numerous key market players. The region accounted for the largest market share in 2023 and is expected to maintain its dominance throughout the forecast period. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by increasing investments in biotechnology and pharmaceutical research, expanding healthcare infrastructure, and the rising adoption of bioinformatics in emerging economies like China and India. Europe's market growth is also significant, supported by substantial funding for genomic research and a strong focus on precision medicine initiatives.

Lifesciences Data Mining and Visualization are becoming increasingly vital in the bioinformatics software market. As the volume of biological data continues to grow exponentially, the need for sophisticated tools to mine and visualize this data is paramount. These tools enable researchers to uncover hidden patterns and insights from complex datasets, facilitating breakthroughs in genomics, proteomics, and other life sciences fields. The integration of advanced data mining techniques with visualization capabilities allows for a more intuitive

The Computational Biology Platform market, currently valued at $218 million in 2025, is projected to experience robust growth, driven by the increasing adoption of cloud-based solutions and the rising need for advanced data analysis in drug discovery, genomics research, and personalized medicine. The market's Compound Annual Growth Rate (CAGR) of 4.9% from 2025 to 2033 indicates a steady expansion, fueled by factors such as decreasing sequencing costs, the exponential growth of biological data, and the increasing sophistication of bioinformatics tools. Large enterprises, particularly pharmaceutical companies and biotechnology firms, are the primary adopters of these platforms, leveraging them for faster drug development and more efficient clinical trials. However, the market also sees significant traction from Small and Medium Enterprises (SMEs) adopting cloud-based solutions for cost-effectiveness and scalability. The preference for cloud-based solutions over on-premises deployments reflects a broader trend toward accessibility, collaboration, and reduced infrastructure management burden. While data security and privacy concerns represent a potential restraint, ongoing advancements in data encryption and compliance regulations are mitigating this risk. The market is geographically diversified, with North America currently holding a substantial market share, but significant growth potential exists in regions like Asia-Pacific, driven by increasing research investment and the rising number of genomics research centers. The competitive landscape is dynamic, featuring both established players like Illumina and newer entrants offering specialized platforms. The success of individual companies will depend on their ability to innovate, adapt to evolving technological advancements, provide robust data security measures, and offer seamless integration with existing research workflows. The continuous development of artificial intelligence (AI) and machine learning (ML) algorithms within these platforms is further accelerating market growth, enabling more accurate predictions, advanced pattern recognition, and improved insights from complex biological data. This technological advancement enhances the platform's effectiveness in accelerating research, drug development processes, and personalized medicine initiatives. The expanding applications of computational biology across various scientific fields are likely to sustain the market’s positive trajectory in the foreseeable future.

Enhancer Explorer application codes

The global bioinformatics sequencing services market is experiencing robust growth, driven by the increasing adoption of next-generation sequencing (NGS) technologies in research and clinical applications. The market's expansion is fueled by several key factors, including the falling cost of sequencing, the rising prevalence of genetic disorders requiring advanced diagnostics, and the growing demand for personalized medicine. The application segments, research and clinical, are both contributing significantly to market growth, with research dominating due to the continuous need for genomic research in various fields like oncology, microbiology, and agriculture. Within the types of services, DNA sequencing currently holds a larger market share compared to RNA sequencing, though RNA sequencing is exhibiting faster growth due to its increasing importance in understanding gene expression and regulation. Key players like Illumina, GENEWIZ, and BGI are actively shaping the market landscape through technological innovation and strategic partnerships, fostering competition and driving down costs. The North American and European regions currently dominate the market, but the Asia-Pacific region is expected to witness significant growth in the coming years, driven by increasing investments in healthcare infrastructure and rising awareness of genetic testing in countries like China and India. While regulatory hurdles and data privacy concerns pose challenges, the overall market outlook remains positive, projecting substantial growth over the forecast period. The competitive landscape is characterized by a blend of established players and emerging companies, each vying for market share through technological advancements, strategic acquisitions, and partnerships. The market is witnessing a shift towards cloud-based bioinformatics platforms, providing scalability and accessibility to researchers and clinicians. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) in bioinformatics analysis is enhancing the speed and accuracy of data interpretation, leading to faster diagnoses and more effective treatment strategies. The increasing demand for comprehensive genomic profiling, coupled with the development of novel sequencing technologies, is expected to further drive market expansion. Although potential restraints exist, such as the high cost of advanced sequencing technologies and the need for specialized expertise in bioinformatics analysis, the overall growth trajectory of the bioinformatics sequencing services market remains strong, promising significant opportunities for players in the industry.

The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

GlycoSuiteDB is a curated database of carbohydrate (glycan) structures sourced from published material. This entry corresponds to a catologue of structures reported in the publication - Neutral core oligosaccharides of bovine submaxillary mucin--use of lead tetraacetate in the cold for establishing branch positions

A set of supplementary data filesAccompanying publication: Evolution of sequence-diverse disordered regions in a protein family: order within the chaos

Supp data file 1Excel file for the 2644 fasciclin domains, names and annotation information. In order to keep names short for phylogenies, FLAs given arbitrary identifier numbers, and fasciclin domains within them indicated by their (e.g. “>X1234_FLA.2.3” -> Fasciclin domain cluster 1, arbitrary FLA identifier number 1234, FLA fasciclin domain 2 out of 3). Numbers and colours given for fasciclin, AG, non-AG and inter-proline clusters.Fields: name = sequence name (constructed as: G[fas.clust] X[number] fas [fas.count] of [fas.max] ; also used in alignments and phylogenies).number = Arbitrary ID number for the FLA sequence· Accession = Phytosome gene sequence ID for the FLA sequencefas.count = Which fasciclin domain is this within the FLA sequencefas.max = How many total fasciclin domains are in the FLA sequencefas.clust(PCA) = Initial cluster based on PCA+MClust of fasciclin domain sequencefas.clust = Cluster based on UMAP+HDBSCAN of fasciclin domain sequence (0=no cluster assigned, 1=type A, 2=type B, etc.)agreg.clust = Cluster based on UMAP+HDBSCAN of arabinogalactan regions (0=no cluster assigned,1=type a, 2=type b, etc.)nagreg.clust = Cluster based on UMAP+HDBSCAN of non-arabinogalactan non-fasciclin regions (0=no cluster assigned,1=type a, 2=type b, etc.)interP.clust = Cluster based on UMAP+HDBSCAN of inter-proline distance (0=no cluster assigned,1=type a, 2=type b, etc.)genus & species = Taxonomy of the organism containing the sequencetax.name = Broad taxonomic group of organism containing the sequence (not necessarily a monophyletic group)[x].col = colour used in diagrams for sequences in that clusterngly.site.[x] = Boolean (true/false) of whether the sequence contains an nglycosylation motif at that position in the sequence. Includes a number to indicate domain within a FLA with 2-fasciclin domains (see figs 2 & S8 for positions)Supp data file 2Multiple sequence alignments as fasta files for all 2644 fasciclin domains, as well as separately for each cluster A-R.Naming:Sequence names = sequence name (constructed as: G[fas.clust] X[number] fas [fas.count] of [fas.max] ; see supp data file 1 fields)File names = Cluster based on UMAP+HDBSCAN of fasciclin domain sequence (0=no cluster assigned, 1=type A, 2=type B, etc.)Supp data file 3Phylogenies as newick files for all 2644 fasciclin domains, as well as separately for each cluster A-R.Naming:Sequence names = sequence name (constructed as: G[fas.clust] X[number] fas [fas.count] of [fas.max] ; see supp data file 1 fields)File names = Cluster based on UMAP+HDBSCAN of fasciclin domain sequence (0=no cluster assigned, 1=type A, 2=type B, etc.)Supp data file 4An [R] script to perform the analyses shown in the publication. See also github repo TS404/FLAnnotator.

Rapid species radiations present difficulties for phylogenetic reconstruction due to lack of phylogenetic information and processes such as deep coalescence/incomplete lineage sorting and hybridization. Phylogenomic data can overcome some of these difficulties. In this study, we use Anchored Hybrid Enrichment (AHE) nuclear phylogenomic data and mitochondrial genomes recovered from AHE bycatch with several concatenated and coalescent approaches to reconstruct the poorly-resolved radiation of the New Zealand cicada species in the genera Kikihia Dugdale and Maoricicada Dugdale. Compared to previous studies using only three to five Sanger-sequenced genes, we find increased resolution across our phylogenies, but several branches remain unresolved due to topological conflict among genes. Some nodes that are strongly supported by traditional support measures like bootstraps and posterior probabilities still show significant gene and site concordance conflict. Additionally, we find strong mito-...

The global gene expression software market is experiencing robust growth, projected to reach $125.5 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 7.5% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing prevalence of genomic research and personalized medicine necessitates advanced analytical tools for interpreting complex gene expression data. Secondly, the growing adoption of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, driving market penetration, especially among smaller research organizations and hospitals with limited IT infrastructure. Thirdly, continuous technological advancements in sequencing technologies and bioinformatics are leading to the development of more sophisticated and user-friendly gene expression software, further accelerating market adoption. The market is segmented by software type (web-based and cloud-based) and application (hospitals and health systems, research organizations, and others). Major players like Agilent Technologies, QIAGEN, Illumina, and others are driving innovation and competition within this dynamic landscape. The market's growth is geographically diverse, with North America currently holding a significant share due to established research infrastructure and early adoption of advanced technologies. However, rapidly developing economies in Asia Pacific, particularly China and India, are poised to exhibit strong growth in the coming years, driven by increasing investments in healthcare research and infrastructure development. Europe is another significant market, fueled by substantial government funding for genomics research and a thriving biotechnology sector. While data privacy regulations and the complexity of gene expression analysis pose some challenges, the overall market outlook remains positive, driven by the inherent value of gene expression analysis in various fields, ranging from drug discovery to disease diagnosis.

Recommended contents for bioinformatics courses embedded in a study path belonging to an MSc degree in the computer science-engineering scientific area.

GlycoSuiteDB is a curated database of carbohydrate (glycan) structures sourced from published material. This entry corresponds to a catologue of structures reported in the publication - Determination of the branch location of extra N-acetyllactosamine units in sialo N-linked tetraantennary oligosaccharides.

The digital biology market is experiencing significant growth, driven by the convergence of biology and computational technologies. Between 2019 and 2024, the market likely exhibited a robust Compound Annual Growth Rate (CAGR), let's assume a conservative estimate of 15% based on industry trends in related sectors like bioinformatics and drug discovery. This growth is fueled by several key factors. Firstly, advancements in high-throughput sequencing, genomics, and proteomics are generating massive datasets that require sophisticated computational tools for analysis and interpretation. Secondly, the increasing adoption of artificial intelligence (AI) and machine learning (ML) in drug discovery and development is accelerating the identification and validation of drug targets, leading to faster and more efficient development cycles. Furthermore, the rising prevalence of chronic diseases globally is further driving demand for innovative diagnostic and therapeutic solutions enabled by digital biology tools. Companies like DUNA Bioinformatics, Precigen, Dassault Systèmes, Genedata AG, and Simulations Plus are at the forefront of this innovation, developing and deploying cutting-edge software and platforms. The market is segmented by application (drug discovery, diagnostics, personalized medicine, etc.), technology (AI/ML, big data analytics, simulation software), and end-user (pharmaceutical companies, biotech firms, research institutions). Looking ahead to 2033, the digital biology market is poised for continued expansion. While predicting precise figures is challenging without complete data, assuming a slightly moderated but still robust CAGR of 12% from 2025 to 2033, based on the anticipated maturation of some technologies and potential market saturation in certain segments, presents a reasonable forecast. This growth will be influenced by ongoing technological advancements, increasing investments in research and development, and expanding collaborations between technology providers and life science companies. However, challenges remain, including the high cost of implementing digital biology solutions, the need for robust data security and privacy protocols, and the complexity of integrating diverse data sources. Nevertheless, the long-term outlook for the digital biology market remains exceptionally positive, promising transformative advancements in healthcare and other related fields.

Detecting the signature of selection in coding sequences and associating it with shifts in phenotypic states can unveil genes underlying complex traits. Of the various signatures of selection exhibited at the molecular level, changes in the pattern of selection at protein coding genes have been of main interest. To this end, phylogenetic branch-site codon models are routinely applied to detect changes in selective patterns along specific branches of the phylogeny. Many of these methods rely on a pre-specified partition of the phylogeny to branch categories, thus treating the course of trait evolution as fully resolved and assuming that phenotypic transitions have occurred only at speciation events. Here we present TraitRELAX, a new phylogenetic model that alleviates these strong assumptions by explicitly accounting for the uncertainty in the evolution of both trait and coding sequences. This joint statistical framework enables the detection of changes in selection intensity upon repeated trait transitions. We evaluated the performance of TraitRELAX using simulations and then applied it to two case studies. Using TraitRELAX, we found an intensification of selection in the primate SEMG2 gene in polygynandrous species compared to species of other mating forms, as well as changes in the intensity of purifying selection operating on sixteen bacterial genes upon transitioning from a free-living to an endosymbiotic lifestyle. Funding provided by: Edmond J. Safra Center for Bioinformatics*Crossref Funder Registry ID: Award Number: Funding provided by: Koret-UC Berkeley-Tel Aviv University Initiative in Computational Biology and Bioinformatics*Crossref Funder Registry ID: Award Number:

Dataset Summary

This dataset contains 4803 question/answer pairs extracted from the BioStars website. The site focuses on bioinformatics, computational genomics, and biological data analysis.

  Dataset Structure





  Data Fields

The data contains INSTRUCTION, RESPONSE, SOURCE, and METADATA fields. The format is described for LAION-AI/Open-Assistant

  Dataset Creation





  Curation Rationale

Questions were included if they were an accepted answer and the… See the full description on the dataset page: https://huggingface.co/datasets/cannin/biostars_qa.