Slides and a handout to introduce and lead an activity aimed at introducing scientists (it was delivered to a group of people interested in bioinformatics) to ways of thinking about professional networking, focused on helping them use it in their career development. The activity focuses on initiating contact with people you don't know at scientific meetings. The material was delivered at the 11th Heidelberg Unseminar in Bioinformatics Meeting in May 2014.

The Bioinformatics Cloud Platform market is experiencing robust growth, driven by the increasing volume of biological data generated from genomics research, personalized medicine initiatives, and drug discovery programs. The need for scalable, cost-effective, and secure data storage and analysis solutions is fueling the adoption of cloud-based platforms. This market is segmented by service type (SaaS, PaaS, IaaS) and application (academic & research, pharmaceutical, others). While precise market size figures are not provided, based on industry reports and observed growth in related sectors like cloud computing and genomics, we can estimate the 2025 market size to be approximately $5 billion, with a Compound Annual Growth Rate (CAGR) of 20% projected from 2025 to 2033. This strong CAGR reflects the continuous advancements in sequencing technologies, the expansion of big data analytics in life sciences, and the growing adoption of cloud computing across various organizations. The pharmaceutical sector is a major contributor to this growth, driven by the need for faster and more efficient drug development pipelines that leverage powerful computational capabilities. Academic and research institutions also play a crucial role in market expansion through their active engagement in genomic research and data sharing initiatives. The market's growth is further propelled by several key trends, including the increasing accessibility of cloud-based bioinformatics tools, the development of advanced analytics techniques like AI and machine learning for data interpretation, and the rising emphasis on data security and compliance within the life sciences industry. However, challenges such as data privacy concerns, the complexity of integrating diverse data sources, and the need for specialized expertise to effectively utilize these platforms represent potential restraints. Nevertheless, the long-term outlook for the Bioinformatics Cloud Platform market remains exceptionally positive, driven by the continuous rise in genomic data and the increasing reliance on cloud-based solutions for efficient data management and analysis within the life sciences domain. Major players like Amazon Web Services, Google Cloud, Microsoft Azure, and specialized bioinformatics companies are actively competing and innovating within this rapidly expanding space.

The global Biological Data Analysis Services market is experiencing robust growth, driven by the increasing volume of biological data generated from high-throughput technologies like next-generation sequencing and advanced imaging techniques. The market's expansion is further fueled by the rising demand for personalized medicine, the growing adoption of bioinformatics tools and cloud-based solutions, and increasing investments in research and development across various sectors including pharmaceutical, biotechnology, and academic research. Key application areas such as biomarker identification, biological modeling, and image analysis are witnessing significant traction, contributing substantially to the market's overall growth. The diverse range of services offered, encompassing statistical data analysis and programming, data visualization, and structural biology, caters to the varied needs of researchers and organizations. Segments like biomarker identification and biological modeling are anticipated to exhibit faster growth compared to others owing to their crucial role in drug discovery and development. North America and Europe currently dominate the market, owing to established research infrastructure and higher healthcare expenditure, but the Asia-Pacific region is projected to show rapid growth due to increasing investments in life sciences research and development, and the expanding biotechnology sector. Competitive landscape analysis reveals a mix of large multinational corporations and specialized service providers. While established players like Eurofins Scientific leverage their extensive network and resources, smaller specialized companies are focusing on niche areas such as specific bioinformatics solutions or particular biological data types, offering innovative and tailored services. This competition is driving innovation and improvement in the quality and accessibility of biological data analysis services. Restraints to market growth include the high cost of advanced analytical tools and the need for specialized expertise to handle complex datasets. However, ongoing technological advancements and the development of user-friendly software are mitigating these challenges. Over the forecast period (2025-2033), continued innovation, particularly in AI and machine learning driven analysis, is expected to further fuel market expansion, leading to improved efficiency and affordability of biological data analysis.

Additional file 1. List of predicted BGCs, list of mQTLs, list of BGCs with mQTLs, list of matches for mQTL genes with profile Hidden Markov Models related to scaffold biosynthesis, overview of LCMS clusters.

The computational biology market is experiencing robust growth, driven by the increasing adoption of advanced technologies like artificial intelligence (AI) and machine learning (ML) in drug discovery and development. The market's Compound Annual Growth Rate (CAGR) of 13.33% from 2019 to 2024 indicates a significant upward trajectory, projected to continue into the forecast period (2025-2033). Key drivers include the rising prevalence of chronic diseases necessitating faster and more efficient drug development processes, the decreasing cost of high-throughput sequencing and data storage, and the increasing availability of large biological datasets fueling advanced computational analyses. The market segmentation reveals strong demand across various applications, including cellular and biological simulations (particularly in genomics and proteomics), drug discovery and disease modeling (with target identification and validation being prominent areas), and preclinical drug development (focused on pharmacokinetics and pharmacodynamics). Clinical trial applications are also significant, spanning Phases I, II, and III. Software tools like databases, analysis software, and specialized infrastructure are critical components, further segmented by service type (in-house vs. contract) and end-user (academic institutions and commercial entities). North America currently holds a significant market share, but Asia-Pacific is projected to witness substantial growth owing to increasing investments in research and development and the rising adoption of computational biology techniques in emerging economies. The competitive landscape is dynamic, with several major players such as Dassault Systèmes SE, Certara, and Schrödinger contributing to innovation. However, the market also includes numerous smaller, specialized companies focusing on niche applications or specific technologies. This competitive landscape encourages continuous innovation, driving the development of more sophisticated software, improved algorithms, and enhanced analytical capabilities. While data limitations exist regarding precise market size figures, extrapolating from the provided CAGR and industry reports suggests a substantial market value currently, exceeding several billion dollars and poised for continued expansion. The focus on precision medicine and personalized therapies further strengthens the long-term growth potential of the computational biology market. Challenges include the complexity of biological systems, the need for robust data validation, and the ethical considerations associated with the use of AI and big data in healthcare. Recent developments include: February 2023: The Centre for Development of Advanced Computing (C-DAC) launched two software tools critical for research in life sciences. Integrated Computing Environment, one of the products, is an indigenous cloud-based genomics computational facility for bioinformatics that integrates ICE-cube, a hardware infrastructure, and ICE flakes. This software will help securely store and analyze petascale to exascale genomics data., January 2023: Insilico Medicine, a clinical-stage, end-to-end artificial intelligence (AI)-driven drug discovery company, launched the 6th generation Intelligent Robotics Lab to accelerate its AI-driven drug discovery. The fully automated AI-powered robotics laboratory performs target discovery, compound screening, precision medicine development, and translational research.. Key drivers for this market are: Increase in Bioinformatics Research, Increasing Number of Clinical Studies in Pharmacogenomics and Pharmacokinetics; Growth of Drug Designing and Disease Modeling. Potential restraints include: Increase in Bioinformatics Research, Increasing Number of Clinical Studies in Pharmacogenomics and Pharmacokinetics; Growth of Drug Designing and Disease Modeling. Notable trends are: Industry and Commercials Sub-segment is Expected to hold its Highest Market Share in the End User Segment.

Statistics of the simulated reads: quality filtering and de novo assembly.

The Paired Omics Data Platform is a community-based initiative standardizing links between genomic and metabolomics data in a computer readable format to further the field of natural products discovery. The goals are to link molecules to their producers, find large scale genome-metabolome associations, use genomic data to assist in structural elucidation of molecules, and provide a centralized database for paired datasets. This dataset contains the projects in http://pairedomicsdata.bioinformatics.nl/.

The JSON documents adhere to the http://pairedomicsdata.bioinformatics.nl/schema.json JSON schema.

Comparison of the number of reads classified as viruses by Granberg et al. (Blastn-LCA method) and the number of reads classified as viruses by MetLab with Kraken and vFam methods.

The small RNA sequencing market is experiencing robust growth, driven by the increasing understanding of small RNAs' crucial roles in gene regulation and disease pathogenesis. Advances in sequencing technologies, offering higher throughput, lower costs, and improved accuracy, are significantly fueling market expansion. The biomedical field, particularly oncology and infectious disease research, is the dominant application area, owing to the potential of small RNA biomarkers for early disease detection and personalized medicine. However, the non-medical field, including agriculture and environmental research, is also showing substantial growth as researchers explore the functional roles of small RNAs in various organisms and ecosystems. The market is fragmented, with numerous players ranging from established life science giants like Illumina and Thermo Fisher Scientific to specialized smaller companies. Competitive advantages are based on technological innovation, data analysis capabilities, and comprehensive service offerings. While the high cost of sequencing and specialized bioinformatics expertise can pose challenges, the continuous reduction in sequencing costs and the development of user-friendly analysis tools are mitigating these restraints, paving the way for wider market adoption. The North American market currently holds the largest share, attributable to the presence of well-established research institutions, robust funding for life science research, and early adoption of advanced technologies. However, the Asia-Pacific region is expected to witness the fastest growth, driven by increasing government support for research and development, coupled with a rapidly expanding healthcare infrastructure. Over the forecast period (2025-2033), the market is projected to maintain a strong CAGR, propelled by ongoing technological advancements and the expanding applications of small RNA sequencing across various fields. The competitive landscape is characterized by both large multinational corporations and smaller specialized companies. Strategic partnerships and mergers & acquisitions are frequent occurrences, signifying a consolidation trend in the market. Future market growth will be strongly influenced by the development of novel applications, particularly in areas like liquid biopsy diagnostics and precision medicine. Furthermore, the increasing availability of large-scale RNA sequencing datasets and advancements in bioinformatics tools for data analysis will further propel market growth. Regions with developing healthcare infrastructures and growing research activities will witness accelerated market penetration. Regulatory approvals for diagnostic applications of small RNA sequencing will be crucial in determining the pace of market expansion in the healthcare sector.

Comparison of time and computing resources used by the compared binning methods.

BackgroundThe rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.ResultsWe report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes.ConclusionsWe have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator

Subfamilies of cytochrome P450 proteins have been strongly linked to the metabolism of physiologically disruptive compounds such as alkaloids, terpenoids, and other xenobiotics. Consistent with this function, these genes have adaptively evolved in response to environmental pressures exerted on animals, such as herbivores, that consume elevated amounts of toxic xenobiotics or plant secondary metabolites (PSMs). Theory on evolutionary tradeoffs predicts that highly specialized herbivores should exhibit a relatively narrow toolkit of adaptations to accommodate the concomitantly narrow arrays of PSMs in their diets. The bamboo lemurs of Madagascar (genera Prolemur and Hapalemur) represent an interesting test case for this theory because of their dietary hyper-specialization, as these lemurs consume bamboo and grasses at rates otherwise unseen in the order Primates. To test whether the hyper-specialized folivory of these primates is reflected in a similarly specialized and narrow P450 gene suite, we assembled a dataset of confidently assembled CYP1-3 genes for two species of bamboo lemur and 13 additional lemur species. With this dataset, we tested the predictions that bamboo lemurs would exhibit, first, greater rates of gene loss for xenobiotic-metabolizing P450s and, second, relaxed selection on xenobiotic-metabolizing P450 subfamilies relative to lemurs without such dietary hyper-specialization. We found support for the prediction of gene loss in the CYP2B, CYP2C, CYP2D, CYP2J, and CYP3A subfamilies, all of which encode xenobiotic metabolizers. We inferred relaxation of selection for the CYP1A and CYP2D subfamilies. The CYP2F subfamily exhibited a signal of significant intensification of selection in the bamboo-lemur lineage. The evolution of the P450 genes in bamboo lemurs provides support for the evolutionary tradeoff hypothesis, and we further hypothesize that, rather than adapting to a general array of PSMs, bamboo lemurs have instead adapted to the primary toxin in their diet, the highly potent poison cyanide. Methods Data gathering
In addition to a novel genome assembly for Hapalemur griseus, we mined data from publicly available genome assemblies for 14 species: Prolemur simus (Hawkins et al., 2018), Lemur catta (Palmada-Flores et al., 2022), Eulemur flavifrons and E. macaco (Meyer et al. 2015), Propithecus coquereli (Lowe and Eddy 1997; Guevara et al. 2021), Indri indri (accession number: GCA_004363605.1), Daubentonia madagascariensis (accession number: GCA_004027145.1), Mirza coquereli (accession number: GCA_004024645.1), Mirza zaza (Hunnicutt et al., 2020), and Microcebus murinus (Averdam et al., 2011; Lecompte et al., 2016), as well as the following additional species of mouse lemur: Mic. griseorufus, Mic. mittermeieri, Mic. ravelobensis, and Mic. tavaratra (Hunnicutt et al., 2020). These assemblies, along with any associated annotation files, were downloaded locally and formatted into BLAST databases within Geneious Prime, version 2022.1.1. We located the loci for all annotated CYP1-3 homologs in the L. catta, Prop. coquereli, and Mic. murinus by using the associated annotation (GFF3) files for each. We defined these loci by the non-P450 genes that bounded them; therefore, those surrounding genes were used initially as queries for local BLAST searches. In this way, each locus was linked to two searches per species. The three reference genomes listed above were used because they are all members of separate strepsirrhine families (Lemuridae, Indriidae, and Cheirogaleidae, respectively), and they were each therefore used as a starting point to extract the desired CYP1-3 genes or loci for confamilial species. Ideally, a pair of BLAST searches would return results that included the same scaffold. By locating both BLAST hits on each of these scaffolds, we were able to extract genomic regions that were hypothetically orthologous to those P450 loci in the L. catta assembly. After locating the scaffolds in each assembly corresponding to each P450 locus, we used LASTZ (Harris, 2007) to interrogate the homology of those scaffolds by aligning them to the confirmed P450 locus from the appropriate confamilial reference genome. Positive results from these alignments were checked using the Mauve genome aligner (Darling et al., 2004) on the same sequences. If output from both of these aligners indicated that the reference had homology with the query scaffold(s), then the annotations from the reference genome were used to extract the corresponding sequence in the other species’ genome. In this way, we mined the genome assemblies listed above for as many complete P450 genes loci as we could confidently locate. Inference of gene birth and death For this first portion of the study, we used only species for which each CYP1-3 locus could be wholly collected from a single scaffold or reasonably reconstructed if not found on a single scaffold using the process described above. In order to model the events of gene birth and death in this subset of lemur species, our alignment strategy followed a similar workflow as outlined in previous work with other datasets (Chaney et al., 2018, 2020), but several modifications were made for this project in order to allow for more standardization and automation across subfamilies. First, all of the P450 genes were extracted from each species’ locus according to the annotation file associated with its confamilial reference. Then, all of the genes from a given P450 subfamily were aligned using MAFFT (Katoh & Standley, 2013), and the resulting alignment was stripped of all sites containing any gaps using trimAl (Capella-Gutierrez et al., 2009). After the best-fitting nucleotide substitution model was inferred by jModelTest (Darriba et al., 2012), this stripped alignment was visualized with PhyML 3.0 and the strength of that resulting phylogenetic tree was tested by comparing it to 1000 bootstrap replicates (Guindon et al., 2010). The gene trees constructed with PhyML were then passed to Possvm (Grau-Bové & Sebé-Pedrós, 2021). This program uses the intrinsic information contained in a phylogram to infer speciation and gene-duplication events; it does this using the species-overlap algorithm in the ETE3 toolkit (Huerta-Cepas et al., 2007, 2016). Briefly, this algorithm compares the intersection of species present in both descendants of an internal node of a tree to the union of species present in those descendants; using these values, the algorithm computes a species-overlap score which it then uses to identify each internal node as either a speciation event, having an overlap score, or a duplication event, having a high overlap score (Huerta-Cepas et al., 2007). Once the identities of each node were estimated in this way, we then manually examined each subtree rooted by a node called as a duplication event to infer whether any gene loss had occurred. This was examined on a case-by-case basis using the reasoning that, after a duplication event, each descendant of that node should recapitulate the organismal phylogeny present at the time of duplication. Therefore, any species missing in one of those subtrees must have lost one of the duplicates born in the earlier duplication event as long as the subtree in question was well-resolved in terms of bootstrap support. In cases where multiple species lineages may be absent, we deferred to the parsimonious hypothesis that a loss event would have occurred prior to the divergence of those lineages, rather than a more complicated hypothesis that the same paralog had been independently lost in both species after their split. We visualized the Possvm output using the program Treerecs (Comte et al., 2020) and then, in some cases, manually modified the depicted gene-evolution scenario in order to accommodate the Possvm results. References Averdam, A., Kuschal, C., Otto, N., Westphal, N., Roos, C., Reinhardt, R., & Walter, L. (2011). Sequence analysis of the grey mouse lemur (Microcebus murinus) MHC class II DQ and DR region. Immunogenetics, 63(2), 85–93. https://doi.org/10.1007/s00251-010-0487-3 Capella-Gutierrez, S., Silla-Martinez, J. M., & Gabaldon, T. (2009). trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics, 25(15), 1972–1973. https://doi.org/10.1093/bioinformatics/btp348 Chaney, M. E., Piontkivska, H., & Tosi, A. J. (2018). Retained duplications and deletions of CYP2C genes among primates. Molecular Phylogenetics and Evolution, 125, 204–212. https://doi.org/10.1016/j.ympev.2018.03.037 Chaney, M. E., Romine, M. G., Piontkivska, H., & Tosi, A. J. (2020). Diversifying selection detected in only a minority of xenobiotic-metabolizing CYP1-3 genes among primate species. Xenobiotica, 50. https://doi.org/10.1080/00498254.2020.1785580 Comte, N., Morel, B., Hasić, D., Guéguen, L., Boussau, B., Daubin, V., Penel, S., Scornavacca, C., Gouy, M., Stamatakis, A., Tannier, E., & Parsons, D. P. (2020). Treerecs: An integrated phylogenetic tool, from sequences to reconciliations. Bioinformatics, 36(18), 4822–4824. https://doi.org/10.1093/bioinformatics/btaa615 Darling, A. C. E., Mau, B., Blattner, F. R., & Perna, N. T. (2004). Mauve: Multiple Alignment of Conserved Genomic Sequence With Rearrangements. Genome Research, 14(7), 1394–1403. https://doi.org/10.1101/gr.2289704 Darriba, D., Taboada, G. L., Doalla, R., & Posada, D. (2012). jModelTest 2: More models, new heuristics and parallel computing. Nature Methods, 9(8), 772. Guevara, E. E., Webster, T. H., Lawler, R. R., Bradley, B. J., Greene, L. K., Ranaivonasy, J., Ratsirarson, J., Harris, R. A., Liu, Y., Murali, S., Raveendran, M., Hughes, D. S. T., Muzny, D. M., Yoder, A. D., Worley, K. C., & Rogers, J. (2021). Comparative genomic analysis of sifakas (Propithecus) reveals selection for folivory and high heterozygosity despite endangered status. Science Advances, 7(17), 1–13. https://doi.org/10.1126/sciadv.abd2274 Guindon, S., Dufayard,

Results from variant calling in the three workflows MiSEQ Reporter 2.1.43 (GATK variant caller), CLC Genomics Workbench 5.51 (Probalistic variant caller) and the in-house custom pipeline (Freebayse variant caller). Results are presented as total number of variants in targeted regions and variants per sample.Variant calling

Results from read mapping. Data presented from three bioinformatics workflows; MiSEQ Reporter 2.1.43 (Smith Waterman algorithm mapper), CLC Genomics Workbench 5.51 (default CLC mapper) and in-house custom pipeline (Burrows Wheeler Alignment tool).Read Mapping

Summary of patient characteristics, syndrome criteria and discovered genetic variants. PGL4; Familial Paraganglioma type 4, VHL; Von Hippel Lindau, MEN2; Multiple Endocrine Neoplasia Type 2; and NF1, Neurofibromatosis type 1. (S); Not detected in DNA from peripheral blood.* indicates stop codon.Detected mutations and patient characteristics

In the framework of the C-HPP, our Franco-Swiss consortium has adopted chromosomes 2 and 14, coding for a total of 382 missing proteins (proteins for which evidence is lacking at protein level). Over the last 4 years, the French proteomics infrastructure has collected high-quality data sets from 40 human samples, including a series of rarely studied cell lines, tissue types, and sample preparations. Here we described a step-by-step strategy based on the use of bioinformatics screening and subsequent mass spectrometry (MS)-based validation to identify what were up to now missing proteins in these data sets. Screening database search results (85 326 dat files) identified 58 of the missing proteins (36 on chromosome 2 and 22 on chromosome 14) by 83 unique peptides following the latest release of neXtProt (2014-09-19). PSMs corresponding to these peptides were thoroughly examined by applying two different MS-based criteria: peptide-level false discovery rate calculation and expert PSM quality assessment. Synthetic peptides were then produced and used to generate reference MS/MS spectra. A spectral similarity score was then calculated for each pair of reference-endogenous spectra and used as a third criterion for missing protein validation. Finally, LC–SRM assays were developed to target proteotypic peptides from four of the missing proteins detected in tissue/cell samples, which were still available and for which sample preparation could be reproduced. These LC–SRM assays unambiguously detected the endogenous unique peptide for three of the proteins. For two of these, identification was confirmed by additional proteotypic peptides. We concluded that of the initial set of 58 proteins detected by the bioinformatics screen, the consecutive MS-based validation criteria led to propose the identification of 13 of these proteins (8 on chromosome 2 and 5 on chromosome 14) that passed at least two of the three MS-based criteria. Thus, a rigorous step-by-step approach combining bioinformatics screening and MS-based validation assays is particularly suitable to obtain protein-level evidence for proteins previously considered as missing. All MS/MS data have been deposited in ProteomeXchange under identifier PXD002131.

• Given that sequencing provider provides the analysis. (DOCX)

Sensitivity of targeted next generation sequencing compared to current golden standard (automated Sanger sequencing) covering 5683 basepairs. Results are presented separately for both sequencing runs as well. Filtered and merged results incudes only variants available in both sequencing runs. Pos; positive, neg; negative.Sensitivity and specificity compared to Sanger Sequencing.

Fuzzy Knowledge-base for 51 different biomarkers and their association with mortality risk factors in community dwelling elderlyIf you use this dataset/KB, please cite the following articles:Rizzo L., Majnaric L., Dondio P., Longo L. (2018) An Investigation of Argumentation Theory for the Prediction of Survival in Elderly Using Biomarkers. In: Iliadis L., Maglogiannis I., Plagianakos V. (eds) Artificial Intelligence Applications and Innovations. AIAI 2018. IFIP Advances in Information and Communication Technology, vol 519. Springer, ChamRizzo L., Majnaric L., Longo L. (2018) A Comparative Study of Defeasible Argumentation and Non-monotonic Fuzzy Reasoning for Elderly Survival Prediction Using Biomarkers. In: Ghidini C., Magnini B., Passerini A., Traverso P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science, vol 11298. Springer, Cham

Median science identity and intent to pursue bioinformatics for the Virtual BUILD Research Collaboratory 2020.