PRISMA Checklist. (DOC)

We present GobyWeb, a web-based system that facilitates the management and analysis of high-throughput sequencing (HTS) projects. The software provides integrated support for a broad set of HTS analyses and offers a simple plugin extension mechanism. Analyses currently supported include quantification of gene expression for messenger and small RNA sequencing, estimation of DNA methylation (i.e., reduced bisulfite sequencing and whole genome methyl-seq), or the detection of pathogens in sequenced data. In contrast to previous analysis pipelines developed for analysis of HTS data, GobyWeb requires significantly less storage space, runs analyses efficiently on a parallel grid, scales gracefully to process tens or hundreds of multi-gigabyte samples, yet can be used effectively by researchers who are comfortable using a web browser. We conducted performance evaluations of the software and found it to either outperform or have similar performance to analysis programs developed for specialized analyses of HTS data. We found that most biologists who took a one-hour GobyWeb training session were readily able to analyze RNA-Seq data with state of the art analysis tools. GobyWeb can be obtained at http://gobyweb.campagnelab.org and is freely available for non-commercial use. GobyWeb plugins are distributed in source code and licensed under the open source LGPL3 license to facilitate code inspection, reuse and independent extensions http://github.com/CampagneLaboratory/gobyweb2-plugins.

Access the summary of the Computational Biology market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

The computational biology market is projected to reach $14.37 billion by 2033, exhibiting a CAGR of 8.16% during the forecast period (2023-2033). Key market drivers include the increasing demand for personalized medicine, advancements in high-throughput technologies, and the growing adoption of cloud-based solutions. The market is segmented by application, solution type, deployment mode, end user, technology, and geography. North America is the largest regional market, followed by Europe and Asia Pacific. The increasing investment in research and development and the presence of major pharmaceutical and biotechnology companies contribute to the growth of the market in North America. Europe is also a significant market, driven by favorable government policies and the presence of a large number of research institutions. Asia Pacific is expected to witness the highest growth rate during the forecast period, owing to the increasing demand for healthcare services and the growing adoption of advanced technologies in the region. Market Overview Powered by advancements in data science, artificial intelligence, and cloud computing, the computational biology industry is on track to skyrocket from $13.8 billion in 2022 to $63.6 billion by 2032. The burgeoning healthcare industry, driven by a heightened emphasis on personalized medicine and the development of novel treatments, is fanning the flames of this transformative market. Recent developments include: , Recent developments in the Computational Biology Market highlight significant advancements and collaborations aimed at enhancing research and application in healthcare and biotechnology. Several prominent companies are leveraging artificial intelligence and machine learning to optimize drug discovery processes, enabling faster and more cost-effective development of therapeutics. Additionally, partnerships between academic institutions and biotech firms are increasingly common, fostering innovative projects that harness computational techniques for genomic analysis and personalized medicine. Investment in cloud-based solutions has surged, reflecting the growing need for scalable data management and analysis tools in handling vast biological datasets. As the market evolves, regulatory frameworks are adapting to support these rapid advancements while ensuring ethical considerations are addressed. The momentum created by these factors suggests a robust growth trajectory leading to 2032, as stakeholders recognize the transformative potential of computational biology in enhancing healthcare outcomes and driving significant economic value., Computational Biology Market Segmentation Insights. Key drivers for this market are: Personalized medicine advancements Drug discovery efficiency Genomic data analysis growth AI integration in research Cross-industry collaborations expansion . Potential restraints include: Rising demand for personalized medicine Increasing investment in biotechnology Advancements in genomic sequencing Growing prevalence of chronic diseases Integration of AI in research. .

The global cell biology cloud computing market size was valued at USD 3,747.97 million in 2023 and is expected to grow at a compound annual growth rate (CAGR) of 18.7% during the forecast period 2024-2033. Cloud computing has revolutionized the field of cell biology, enabling researchers to access vast amounts of data and computational resources to analyze and interpret complex biological processes. Key drivers of this market growth include the increasing adoption of cloud-based platforms by pharmaceutical and biotechnology companies, the growing volume of biological data generated by high-throughput technologies, and the need for advanced computational tools to analyze and interpret this data. The genomics segment is expected to hold the largest market share during the forecast period, primarily due to the high demand for cloud-based solutions for genomic data analysis. Public cloud computing is expected to dominate the market, owing to its flexibility, scalability, and cost-effectiveness.

We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called Fixseq. We demonstrate that Fixseq substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.

Computational Biology Market was valued at $5.73 Billion in 2023, and is projected to $USD 17.74 Billion by 2032, at a CAGR of 13.39% from 2023 to 2032.

Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modelling to explain patterns of genetic alterations seen in 178 patients with bladder tumours (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signalling, cell cycle and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publically. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signalling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3 activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favour invasiveness. Further, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness.

GINsim archive (zginml) with the model, its annotations and simulation parameters; the SBML file can be imported using any tool supporting the SBML qual format

Warning: the zginml archive should be open using a recent GINsim version (>2.8)

The goal of this project is to developed an open-source componential framework that can be used as a platform of a benchmark tool, that would allow to investigate the performance of bio-inspired olfactorial navigators (i.e. agents that locate an odor source via chemosensation), in a virtual turbulent environment. The open-source componential framework-‘MothPy' (package written in Python) developed by our team allows the user to adjust different indices regarding the behavior of the flyer (e.g. ground speed) or the behavior of the plume (e.g. meandering amplitude, puff spread rate).

The bio-inspired navigators used in this project are male moths, which are known for expertise in locating a volatile odor source (i.e. female moth as the odor-source). The searching behavior of the simulated navigators was defined base on two navigation strategies that has been previously proposed.
The archived data show the flight performance of multiple virtual moth-inspired navigators, in different simulated environments. To provide a comprehensive quantitative comparison we used the bio-statistical analyses commonly used in the neurotheological study of animals' locomotion.

Introduction

The Global Bioinformatics Services Market is poised for substantial growth, projected to increase from USD 2.9 billion in 2023 to USD 10.7 billion by 2033, achieving a compound annual growth rate (CAGR) of 13.9%. This market expansion is fueled by several key factors including technological advancements in genomics and the increasing complexity of biological datasets, which necessitate advanced computational technologies for efficient data management, analysis, and interpretation. These technologies are crucial for advancing medical research and improving patient care, particularly through personalized treatment plans and precision medicine.

Institutions like the Mayo Clinic are significantly contributing to this growth by expanding their bioinformatics services to support translational research and enhance patient care through the integration of large multi-omics data sets. Additionally, prominent educational institutions such as Stanford and Georgetown University are advancing their bioinformatics programs to equip the next generation of professionals with the necessary skills to address complex biomedical challenges using computational and quantitative methods.

The sector is also witnessing a surge in demand within the healthcare and pharmaceutical industries, where bioinformatics tools are integral to drug discovery and disease diagnosis. This demand drives the development of therapeutic strategies and deepens the understanding of disease mechanisms, further boosting the market growth. Research initiatives and collaborations, such as those at Harvard Medical School’s Department of Biomedical Informatics and Stanford's Biomedical Informatics Research division, are key in transforming biomedical data into actionable insights for precision medicine.

In terms of recent industry developments, in January 2024, Qiagen announced a significant expansion of investments into its Qiagen Digital Insights (QDI) business. This expansion, fueled by robust sales of approximately $100 million in 2023, is set to enhance QDI's bioinformatics capabilities, including launching at least five new products and broadening the applications of Artificial Intelligence and Natural Language Processing within the sector.

Furthermore, in January 2023, Agilent Technologies unveiled a major investment of $725 million to double its manufacturing capacity for nucleic acid-based therapeutics, in response to the rapid growth in the therapeutic oligonucleotides market, projected to reach $2.4 billion by 2027. This expansion will introduce two new manufacturing lines to meet the escalating demand for siRNA, antisense, and CRISPR guide RNA molecules, reinforcing Agilent's market presence and capacity in this fast-evolving field.

The amount of data used in phylogenetics has grown explosively in the recent years and many phylogenies are inferred with hundreds or even thousands of loci and many taxa. These modern phylogenomic studies often entail separate analyses of each of the loci in addition to multiple analyses of subsets of genes or concatenated sequences. Computationally efficient tools for handling and computing properties of thousands of single-locus or large concatenated alignments are needed. Here I present AMAS (Alignment Manipulation And Summary), a tool that can be used either as a stand-alone command-line utility or as a Python package. AMAS works on amino acid and nucleotide alignments and combines capabilities of sequence manipulation with a function that calculates basic statistics. The manipulation functions include conversions among popular formats, concatenation, extracting sites and splitting according to a pre-defined partitioning scheme, creation of replicate data sets, and removal of taxa. The statistics calculated include the number of taxa, alignment length, total count of matrix cells, overall number of undetermined characters, percent of missing data, AT and GC contents (for DNA alignments), count and proportion of variable sites, count and proportion of parsimony informative sites, and counts of all characters relevant for a nucleotide or amino acid alphabet. AMAS is particularly suitable for very large alignments with hundreds of taxa and thousands of loci. It is computationally efficient, utilizes parallel processing, and performs better at concatenation than other popular tools. AMAS is a Python 3 program that relies solely on Python’s core modules and needs no additional dependencies. AMAS source code and manual can be downloaded from http://github.com/marekborowiec/AMAS/ under GNU General Public License.

Supplementary Table S10

The dataset comes from a study which assessed the complexity of 3′ UTRs (Three prime untranslated regions)relative to that of protein-coding sequences, by comparing the extent to which segmental substructures can be detected within these two genomic fractions based on sequence composition and conservation.

The dataset presents the segmentation characteristics of models following an investigation of the stability of segment classes.

According to Cognitive Market Research, the Global Bioinformatics Services Market Size will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

• The global Bioinformatics services Market will expand significantly by XX% CAGR between 2024 and 2031.

• Based on technology, Because of the growing number of platform applications and the need for improved tools for drug development, the bioinformatics platforms segment dominated the market.

• In terms of service type, The sequencing services segment held the largest share and is anticipated to grow over the coming years

• Based on application, The genomic segment dominated the bioinformatics market

• Based on End-user, academic institutes and research centers segment hold the largest share.

• Based on speciality segment, The medical bioinformatics segment holds the large share and is anticipated to expand at a substantial CAGR during the forecast period.

• The North America region accounted for the highest market share in the Global Bioinformatics Services Market. CURRENT SCENARIO OF THE BIOINFORMATICS SERVICES

Driving Factors of the Bioinformatics Services Market

Expansive uses of bioinformatics across multiple sectors is propelling the market's growth.

Several industries, such as the food, bioremediation, agriculture, forensics, and consumer industries, are also using bioinformatics services to improve the quality of their products and supply chain processes. Companies in a variety of sectors are rapidly utilizing bioinformatics services such as data integration, manipulation, lead generation, data management, in silico analysis, and advanced knowledge discovery.

• Bioinformatics Approaches in Food Sciences

In order to meet the needs of food production, food processing, enhancing the quality and nutritional content of food sources, and many other areas, bioinformatics plays a significant role in forecasting and evaluating the intended and undesired impacts of microorganisms on food, genomes, and proteomics research. Furthermore, bioinformatics techniques can be applied to produce crops with high yields and resistance to disease, among other desirable qualities. Additionally, there are numerous databases with information about food, including its components, nutritional value, chemistry, and biology.

Genome Canada is proud to partner with five Institutes where there are five funding pools within this opportunity and Genome Canada is partnering on the Bioinformatics, Computational Biology and Health Data Sciences pool. (Source:https://genomecanada.ca/genome-canada-partners-with-cihr-to-launch-health-research-training-platform-2024-25/)

• Bioinformatics in agriculture

Bioinformatics is becoming more and more crucial in the gathering, storing, and processing of genomic data in the field of agricultural genomics, or agri-genomics. Generally referred to as agri-informatics, some of the various applications of bioinformatics tools and methods in agriculture focus on improving plant resistance against biotic and abiotic stressors as well as enhancing the nutritional quality in depleted soils. Beyond these uses, computer software-assisted gene discovery has enabled researchers to create focused strategies for seed quality enhancement, incorporate extra micronutrients into plants for improved human health, and create plants with phytoremediation potential.

India/UK-based Agri-Genomics startup, Piatrika Biosystems has raised $1.2 Million in a seed round led by Ankur Capital. The company is bringing sustainable seeds and agri chemicals to market faster and cheaper. The investment will be used to build a strong Product Development team, also for more profound research, and to accelerate the productionising and commercialization of MVP. (Source:https://pressroom.icrisat.org/agri-genomics-startup-piatrika-biosystems-raises-12-million-in-seed-funding-led-by-ankur-capital)

This expansion in the application areas of bioinformatics services is likely to drive the overall market growth. Bioinformatics services such as data integration, manipulation, lead discovery, data management, in silico analysis, and advanced knowledge discovery are increasingly being adopted by companies across various industries.&...

Supporting Methods (MD simulations; Moving block bootstrapping of MD simulations; Contributions for specific information measures; Identifying high information residues); Supporting Discussion (Efficient information transmission; Normalizing mutual information; Negative co-information; Analysis of the K1,4 network; Control study); Tables S1–S4; Figures S1–S11; Supporting References. (PDF)

Internal states can profoundly alter the behavior of animals. A quantitative understanding of the behavioral changes upon metabolic challenges is key to a mechanistic dissection of how animals maintain nutritional homeostasis. We used an automated video tracking setup to characterize how amino acid and reproductive states interact to shape exploitation and exploration decisions taken by adult Drosophila melanogaster. We find that these two states have specific effects on the decisions to stop at and leave proteinaceous food patches. Furthermore, the internal nutrient state defines the exploration-exploitation trade-off: nutrient-deprived flies focus on specific patches while satiated flies explore more globally. Finally, we show that olfaction mediates the efficient recognition of yeast as an appropriate protein source in mated females and that octopamine is specifically required to mediate homeostatic postmating responses without affecting internal nutrient sensing. Internal states therefore modulate specific aspects of exploitation and exploration to change nutrient selection.

Abbreviations: FEM, fixed-effects model; REM, random-effects model. EOAD, early-onset Alzheimer’s Disease; LOAD, late-onset Alzheimer’s Disease.rs6265: Codominant model, A vs. G; rs2030324: Codominant model, T vs C.

Stable distribution function fitting parameters.

Statistical analyses on the groups (contingent positive (CP), negative (CN) and sham) differences in performance averaged over sessions during motor imagery without feedback (MIT) and with proprioceptive feedback (MIT&F), active and passive movement and rest. The performance measures were the percent of time moving the orthosis (PercT), maximum consecutive time moving the orthosis per trial (MaxC), number of orthosis movement onsets (NOns), latency to the first orthosis Onset (Lat) and reaching target performance (ReachT) per session. Statistically significant values (p

Given a -value (first column), the quantiles show the result of each test for which -values are below the quantile.