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Results for “bioinformatics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 19 recordsLinked to original sources

bioRxiv2017

Reproducible Bioinformatics Project: A community for reproducible bioinformatics analysis pipelines

BackgroundReproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and a list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools and/or of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues.\n\nResultsTo address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate her/his workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover she/he has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization.\n\nConclusionsReproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.

bioinformatics↗

bioRxiv2024

BioInformatics Agent (BIA): Unleashing the Power of Large Language Models to Reshape Bioinformatics Workflow

Bioinformatics plays a crucial role in understanding biological phenomena, yet the exponential growth of biological data and rapid technological advancements have heightened the barriers to in-depth exploration of this domain. Thereby, we propose Bio-Informatics Agent (BIA), an intelligent agent leveraging Large Language Models (LLMs) technology, to facilitate autonomous bioinformatic analysis through natural language. The primary functionalities of BIA encompass extraction and processing of raw data and metadata, querying both locally deployed and public databases for information. It further undertakes the formulation of workflow designs, generates executable code, and delivers comprehensive reports. Focused on the single-cell RNA sequencing (scRNA-seq) data, this paper demonstrates BIAs remarkable proficiency in information processing and analysis, as well as executing sophisticated tasks and interactions. Additionally, we analyzed failed executions from the agent and demonstrate prospective enhancement strategies including selfrefinement and domain adaptation. The future outlook includes expanding BIAs practical implementations across multi-omics data, to alleviating the workload burden for the bioinformatics community and empowering more profound investigations into the mysteries of life sciences. BIA is available at: https://github.com/biagent-dev/biagent.

bioinformatics↗

bioRxiv2020

From bioinformatics user to bioinformatics engineer: a report

Teaching computer programming is not a simple task and it is challenging to introduce the concepts of programming in graduate programs of other fields. Little efforts have been made on engaging students in computational development after programming trainings. An emerging need is to establish subjects of bioinformatics and programming languages in genetics and molecular biology graduate programs, when students in these degree programs are immersed in a sea of genomic and transcriptomic data, which demands proficient computational treatment. I report an empirical guideline to introduce programming languages and recommend Python as first language for graduate programs in which students were from genetics and molecular biology backgrounds. Including the development of programming solutions related to graduate students' research activities may improve programming skills and better engagement. These results suggest that the applied approach leads to enhanced learning of introductory to autonomy in highly advanced programming concepts by graduate students. This guide should be extended for other research programs.

scientific communication and education↗

bioRxiv2018

Lightweight bioinformatics: evaluating the utility of Single Board Computer (SBC) clusters for portable, scalable Real-Time Bioinformatics in fieldwork environments via benchmarking.

The versatility of the current DNA sequencing platforms and the development of portable, nanopore sequencers means that it has never been easier to collect genetic data for unknown sample ID. In fact, the distinction between fieldwork and the laboratory is becoming blurred since genome-scale data can now be collected in challenging conditions in a matter of hours. However, the full scientific and societal benefits of these new methods can only be realised with equally rapid and portable analyses. At present, field-based analyses of genomic data, despite advances in computing technology, remain problematic; laptop computers are relatively expensive and limited in scalability, while cloud- and cluster-based analyses depend, for the time being, on sufficiently reliable high-bandwidth data uplinks to transmit primary data for analysis.\n\nSingle board computers (SBCs), such as the Raspberry Pi, offer a potential solution to this problem: while less powerful than their laptop cousins, their very individual low cost and power consumption mean modest arrays of SBCs could be used for field-based preprocessing, or complete analyses or primary data. In this study we investigate the performance of one SBC, the Pi 3 Model B+, on a range of typical field-sequencing tasks versus laptop and cloud-based form-factors. Our data analysis pipeline has been made available as a workflow on Github for simple, scalable deployment for a range of uses.

bioinformatics↗

bioRxiv2017

Bioinformatics Core Competencies for Undergraduate Life Sciences Education

Bioinformatics is becoming increasingly central to research in the life sciences. However, despite its importance, bioinformatics skills and knowledge are not well integrated in undergraduate biology education. This curricular gap prevents biology students from harnessing the full potential of their education, limiting their career opportunities and slowing genomic research innovation. To advance the integration of bioinformatics into life sciences education, a framework of core bioinformatics competencies is needed. To that end, we here report the results of a survey of life sciences faculty in the United States about teaching bioinformatics to undergraduate life scientists. Responses were received from 1,260 faculty representing institutions in all fifty states with a combined capacity to educate hundreds of thousands of students every year. Results indicate strong, widespread agreement that bioinformatics knowledge and skills are critical for undergraduate life scientists, as well as considerable agreement about which skills are necessary. Perceptions of the importance of some skills varied with the respondents degree of training, time since degree earned, and/or the Carnegie classification of the respondents institution. To assess which skills are currently being taught, we analyzed syllabi of courses with bioinformatics content submitted by survey respondents. Finally, we used the survey results, the analysis of syllabi, and our collective research and teaching expertise to develop a set of bioinformatics core competencies for undergraduate life sciences students. These core competencies are intended to serve as a guide for institutions as they work to integrate bioinformatics into their life sciences curricula.\n\nSignificance StatementBioinformatics, an interdisciplinary field that uses techniques from computer science and mathematics to store, manage, and analyze biological data, is becoming increasingly central to modern biology research. Given the widespread use of bioinformatics and its impacts on societal problem-solving (e.g., in healthcare, agriculture, and natural resources management), there is a growing need for the integration of bioinformatics competencies into undergraduate life sciences education. Here, we present a set of bioinformatics core competencies for undergraduate life scientists developed using the results of a large national survey and the expertise of our working group of bioinformaticians and educators. We also present results from the survey on the importance of bioinformatics skills and the current state of integration of bioinformatics into biology education.

bioinformatics↗

bioRxiv2018

A large-scale analysis of bioinformatics code on GitHub

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/githubbioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.\n\nAuthor summaryWe present, to our knowledge, the first large-scale analysis of bioinformatics source code. The purpose of our work is to contribute data to the growing conversation in the bioinformatics community around reproducibility, code quality, and software usability. We analyze a large collection of bioinformatics software projects, identifying relationships between code properties, development activity, developer communities, and software impact. Throughout the work, we compare the large set of projects to a small set of highly popular bioinformatics tools, highlighting features associated with high-profile projects. We make our data and code publicly available to enable others to build upon our analysis or generate new datasets. The significance of our work is to (1) contribute a large base of knowledge to the bioinformatics community about the state of their software, (2) contribute tools and resources enabling the community to conduct their own analyses, and (3) demonstrate that it is possible to systematically analyze large volumes of bioinformatics code. This work and the provided resources will enable a more effective, data-driven conversation around software practices in the bioinformatics community.

bioinformatics↗

bioRxiv2023

A comparison of bioinformatics pipelines for compositional analysis of the human gut microbiome

Investigating the impact of gut microbiome on human health is a rapidly growing area of research. A significant limiting factor in the progress in this field is the lack of consistency between study results, which hampers the correct biological interpretation of findings. One of the reasons is variation of the applied bioinformatics analysis pipelines. This study aimed to compare five frequently used bioinformatics pipelines (NG-Tax 1.0, NG-Tax 2.0, QIIME, QIIME2 and mothur) for the analysis of 16S rRNA marker gene sequencing data and determine whether and how the analytical methods affect the downstream statistical analysis results. Based on publicly available case-control analysis of ADHD and two mock communities, we show that the choice of bioinformatic pipeline does not only impact the analysis of 16S rRNA gene sequencing data but consequently also the downstream association results. The differences were observed in obtained number of ASVs/OTUs (range: 1,958 - 20,140), number of unclassified ASVs/OTUs (range: 210 - 8,092) or number of genera (range: 176 - 343). Also, the case versus control comparison resulted in different results across the pipelines. Based on our results we recommend: i) QIIME1 and mothur when interested in rare and/or low-abundant taxa, ii) NG-Tax1 or NG-Tax2 when favouring stringent artefact filtering, iii) QIIME2 for a balance between two abovementioned points, and iv) to use at least two pipelines to assess robustness of the results. This work illustrates the strengths and limitations of frequently used microbial bioinformatics pipelines in the context of biological conclusions of case-control comparisons. With this, we hope to contribute to "best practice" approaches for microbiome analysis, promoting methodological consistency and replication of microbial findings. Author SummaryStudies increasingly demonstrate the relevance of gut microbiota in understanding human health and disease. However, the lack of consistency between study results is a significant limiting factor of progress in this field. The reasons for this include variation in study design, sample size, bacterial DNA extraction and sequencing method, bioinformatics analysis pipeline and statistical analysis methodology. This paper focuses on the variation generated by bioinformatics pipelines. A choice of a bioinformatic pipeline can influence the assessment of microbial diversity. However, it is unclear to what extent and how the results and conclusion of a case-control study can be influenced. Therefore, we compared the results of a case-control study across different pipelines (applying default settings) while using the same dataset. Our results indicate a lack of consistency across the pipelines. We show that the choice of bioinformatic pipeline not only affects the analysis results of 16S rRNA gene sequencing data from the gut microbiome, but also the associated conclusions for the case-control study. This means different conclusions would be drawn from the same data analysed with different bioinformatic pipeline.

bioinformatics↗

bioRxiv2025

Evaluating peer-to-peer bioinformatics education: a case study of student learning outcomes and community impact in an undergraduate multi-omic data analysis course

Computational methodology has become ubiquitous in biomedical research with the rise of big data analysis and popularity of artificial intelligence and machine learning. However, undergraduate bioinformatics education has largely struggled to keep pace with the demand for bioinformatics skills, due to a combination of social and resource-based barriers. In this case study, we discuss the application and outcomes of Multi-Omic Data Analysis, a peer-to-peer learning-based undergraduate bioinformatics course offered by the Department of Quantitative and Computational Biology at the University of Southern California. Over eight semesters, a cohort of student instructors taught 2-3 weekly lectures to 107 undergraduate students in the Quantitative Biology Bachelor of Science degree program. Lectures covered a range of topics, including R and Python data analysis, scientific communication, and general research readiness as undergraduate students. We find that bioinformatics education courses structured around peer-to-peer learning have great potential to overcome many of the obstacles to comprehensive undergraduate bioinformatics education, and provide additional benefits related to student cohesion and community. We further discuss the longevity and feasibility of such courses, both specific to our program and in undergraduate universities at large. Author SummaryThere is a growing need for accessible and beginner-friendly bioinformatics education at the undergraduate level. Traditional coursework often fails to meet this demand due to disciplinary separation between biology and computer science, as well as limited institutional resources. We evaluate a unique approach involving a student-led undergraduate bioinformatics course at the University of Southern California to understand how peer-to-peer learning influences student development in computational biology. We examined over 100 students across eight academic semesters. Students varied in prior coding experience and research exposure. Through survey responses, we found that students left the course feeling more confident and prepared for research in faculty labs. Students reported improved competence in bioinformatics skills compared to before they took the course. We found that a major reason for this success was the peer-to-peer learning format. Students mentioned that learning from fellow students made the material more approachable and created a supportive community where they felt comfortable asking questions. Our work demonstrates that student-led initiatives can be a highly effective solution for filling critical gaps in university curricula, making bioinformatics education more accessible.

scientific communication and education↗

bioRxiv2017

Barriers to Integration of Bioinformatics into Undergraduate Life Sciences Education

Bioinformatics, a discipline that combines aspects of biology, statistics, and computer science, is increasingly important for biological research. However, bioinformatics instruction is rarely integrated into life sciences curricula at the undergraduate level. To understand why, the Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE, \"nibbles\") recently undertook an extensive survey of life sciences faculty in the United States. The survey responses to open-ended questions about barriers to integration were subjected to keyword analysis. The barrier most frequently reported by the ~1,260 respondents was lack of faculty training. Faculty at associates-granting institutions report the least training in bioinformatics and the least integration of bioinformatics into their teaching. Faculty from underrepresented minority groups (URMs) in STEM reported training barriers at a higher rate than others, although the number of URM respondents was small. Interestingly, the cohort of faculty with the most recently awarded PhD degrees reported the most training but were teaching bioinformatics at a lower rate than faculty who earned their degrees in previous decades. Other barriers reported included lack of student interest in bioinformatics; lack of student preparation in mathematics, statistics, and computer science; already overly full curricula; and limited access to resources, including hardware, software, and vetted teaching materials. The results of the survey, the largest to date on bioinformatics education, will guide efforts to further integrate bioinformatics instruction into undergraduate life sciences education.

scientific communication and education↗

bioRxiv2024

A Bioinformatician, Computer Scientist, and Geneticist lead bioinformatic tool development - which one is better?

The development of accurate bioinformatic software tools is crucial for the effective analysis of complex biological data. This study examines the relationship between the academic department affiliations of authors and the accuracy of the bioinformatic tools they develop. By analyzing a corpus of previously benchmarked bioinformatic software tools, we mapped bioinformatic tools to the academic fields of the corresponding authors and evaluated tool accuracy by field. Our results suggest that "Medical Informatics" outperforms all other fields in bioinformatic software accuracy, with a mean proportion of wins in accuracy rankings exceeding the null expectation. In contrast, tools developed by authors affiliated with "Bioinformatics" and "Engineering" fields tend to be less accurate. However, after correcting for multiple testing, no result is statistically significant (p > 0.05). Our findings reveal no strong association between academic field and bioinformatic software accuracy. These findings suggest that the development of interdisciplinary software applications can be effectively undertaken by any department with sufficient resources and training.

bioinformatics↗

bioRxiv2024

Recommendations for Bioinformatics in Clinical Practice

Next Generation Sequencing (NGS) is increasingly used in clinical diagnostics, largely driven by the success and robustness of Whole Genome Sequencing (WGS). Whereas updated guidelines exist for how to interpret and report on variants that are identified from NGS using bioinformatics pipelines, there is a need for standardised bioinformatics practices for diagnostics to ensure clinical consensus, accuracy, reproducibility and comparability of the results. This article presents consensus recommendations developed by 13 clinical bioinformatics units taking part in the Nordic Alliance for Clinical Genomics (NACG), by expert bioinformaticians working in clinical production. The recommendations are based on clinical practice and focus on analysis types, test and validation, standardisation and accreditation, as well as core competencies and technical management required for clinical bioinformatics operations. Key recommendations include adopting the hg38 genome build as the reference and a standard set of recommended analyses, including the use of multiple tools for structural variant (SV) calling and in-house data sets for filtering recurrent calls. Clinical bioinformatics production should operate under the ISO 15189 standard, utilising off-grid clinical-grade high-performance computing systems, standardised file formats, and strict code version control. Containerized software containers or environment management systems are needed to ensure reproducibility. Pipelines should be rigorously documented and tested for accuracy and reproducibility, minimally covering unit, integration, and end-to-end testing. Standard truth sets such as GIAB and SEQC2 for germline and somatic variant calling, respectively, should be supplemented by recall testing of previously validated clinical cases. Data integrity must be verified using file hashing, and sample identity should be checked via sample fingerprinting and genetically inferred identification markers such as sex and relatedness. Finally, clinical bioinformatics teams should encompass diverse skills, including software development, data management, quality assurance, and domain expertise in human genetics. These recommendations provide a consensus framework for standardising bioinformatics practices across clinical WGS applications and can serve as a practical guide to facilities that are new to large-scale sequencing-based diagnostics, or as a reference for those who already run high-volume clinical production using NGS.

bioinformatics↗

bioRxiv2023

Bioinformatic investigation of discordant sequence data for SARS-CoV-2: insights for robust genomic analysis during pandemic surveillance

The COVID-19 pandemic has necessitated the rapid development and implementation of whole genome sequencing (WGS) and bioinformatic methods for managing the pandemic. However, variability in methods and capabilities between laboratories has posed challenges in ensuring data accuracy. A national working group comprising 18 laboratory scientists and bioinformaticians from Australia and New Zealand was formed to improve data concordance across public health laboratories (PHLs). One effort, presented in this study, sought to understand the impact of methodology on consensus genome concordance and interpretation. Data were retrospectively obtained from the 2021 Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP) SARS-CoV-2 WGS proficiency testing program (PTP), which included 11 participating Australian laboratories. The submitted consensus genomes and reads from eight contrived specimen were investigated, focusing on discordant sequence data, and findings were presented to the working group to inform best practices. Despite using a variety of laboratory and bioinformatic methods for SARS-CoV-2 WGS, participants largely produced concordant genomes. Two participants returned five discordant sites in a high Ct replicate which could be resolved with reasonable bioinformatic quality thresholds. We noted ten discrepancies in genome assessment that arose from nucleotide heterogeneity at three different sites in three cell-culture derived control specimen. While these sites were ultimately accurate after considering the participants bioinformatic parameters, it presented an interesting challenge for developing standards to account for intrahost single nucleotide variation (iSNV). Observed differences had little to no impact on key surveillance metrics, lineage assignment and phylogenetic clustering, while genome coverage <90% affected both. We recommend PHLs bioinformatically generate two consensus genomes with and without ambiguity thresholds for quality control and downstream analysis, respectively, and adhere to a minimum 90% genome coverage threshold for inclusion in surveillance interpretations. We also suggest additional PTP assessment criteria, including primer efficiency, detection of iSNVs, and minimum genome coverage of 90%. This study underscores the importance of multidisciplinary national working groups in informing guidelines in real time for bioinformatic quality acceptance criteria. It demonstrates the potential for enhancing public health responses through improved data concordance and quality control in SARS-CoV-2 genomic analysis during pandemic surveillance. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Impact statementAmidst the COVID-19 pandemic, a unique collaboration between a national multidisciplinary working group and a quality assurance program facilitated ongoing development of standardized quality control criteria and analysis methods for high-quality SARS-CoV-2 genomic approaches across Australia. With this article, we shed light on the robustness of amplicon sequencing and analysis methods to produce highly concordant genomes, while also presenting additional assessment criteria to guide laboratories in identifying areas for improvement. Insights from this nationwide collaboration underscore the need for real-time knowledge-sharing and iterative refinements to quality standards, particularly as situations and methods evolve during a pandemic. While the spotlight is on SARS-CoV-2, the analyses and findings have universal implications for genomic surveillance during infectious disease outbreaks. As WGS becomes increasingly central in outbreak surveillance, continuous evaluation and collaboration, like that described here, are vital to ensure data accuracy and inform future public health responses.

genomics↗

bioRxiv2019

Toward standards in clinical microbiome studies: comparison of three DNA extraction methods and two bioinformatic pipelines

When studying the microbiome using next generation sequencing, DNA extraction method, sequencing procedures and bioinformatic processing are crucial to obtain reliable data. Method choice has been demonstrated to strongly affect the final biological interpretation. We assessed the performance of three DNA extraction methods and two bioinformatic pipelines for bacterial microbiota profiling through 16S rRNA gene amplicon sequencing, using positive and negative controls for DNA extraction and sequencing, and eight different types of high- or low-biomass samples. Performance was evaluated based on quality control passing, DNA yield, richness, diversity and compositional profiles. All DNA extraction methods retrieved the theoretical relative bacterial abundance with maximum three-fold change, although differences were seen between methods, and library preparation and sequencing induced little variation. Bioinformatic pipelines showed different results for estimating richness, but diversity and compositional profiles were comparable. DNA extraction methods were successful for feces and oral swabs and variation induced by DNA extraction methods was lower than inter-subject (biological) variation. For low-biomass samples, a mixture of genera present in negative controls and sample-specific genera, possibly representing biological signal, were observed. We conclude that the tested bioinformatic pipelines perform equally with pipeline-specific advantages and disadvantages. Two out of three extraction methods performed equally well, while one method was less accurate regarding retrieval of compositional profiles. Lastly, we demonstrate the importance of including negative controls when analyzing low bacterial biomass samples.\n\nIMPORTANCEMethod choice throughout the workflow of a microbiome study, from sample collection to DNA extraction and sequencing procedures, can greatly affect results. This study evaluated three different DNA extraction methods and two bioinformatic pipelines by including positive and negative controls, and various biological specimens. By identifying an optimal combination of DNA extraction method and bioinformatic pipeline use, we hope to contribute to increased methodological consistency in microbiome studies. Our methods were not only applied to commonly studied samples for microbiota analysis, e.g. feces, but also for more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g. urine and tumor biopsies) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiome studies, especially when low bacterial biomass is expected.

microbiology↗

bioRxiv2016

CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community

The increasing availability and decreasing cost of high-throughput sequencing has transformed academic medical microbiology, delivering an explosion in available genomes while also driving advances in bioinformatics. However, many microbiologists are unable to exploit the resulting large genomics datasets because they do not have access to relevant computational resources and to an appropriate bioinformatics infrastructure. Here, we present the Cloud Infrastructure for Microbial Bioinformatics (CLIMB) facility, a shared computing infrastructure that has been designed from the ground up to provide an environment where microbiologists can share and reuse methods and data.\n\nDATA SUMMARYThe paper describes a new, freely available public resource and therefore no data has been generated. The resource can be accessed at http://www.climb.ac.uk. Source code for software developed for the project can be found at http://github.com/MRC-CLIMB/\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.\n\nIMPACT STATEMENTTechnological advances mean that genome sequencing is now relatively simple, quick, and affordable. However, handling large genome datasets remains a significant challenge for many microbiologists, with substantial requirements for computational resources and expertise in data storage and analysis. This has led to fragmentary approaches to software development and data sharing that reduce the reproducibility of research and limits opportunities for bioinformatics training. Here, we describe a nationwide electronic infrastructure that has been designed to support the UK microbiology community, providing simple mechanisms for accessing large, shared, computational resources designed to meet the bioinformatic needs of microbiologists.

Bioinformatics↗

bioRxiv2021

BIAPSS - BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences

Liquid-liquid phase separation (LLPS) has recently emerged as a cornerstone mechanism underlying the biogenesis of membraneless organelles (MLOs). However, a quantitative molecular grammar of protein sequences that controls the LLPS remains poorly understood. The progress in this field is hampered by the insufficiency of comprehensive databases and associated computational infrastructure for targeting biophysical and statistical analysis of phase separating biopolymers. Therefore, we have created a novel open-source web platform named BIAPSS (BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences) which contains interactive data analytic tools in combination with a comprehensive repository of bioinformatic data for on-the-fly exploration of sequence-dependent properties of proteins with known LLPS behavior. BIAPSS includes a residue-resolution biophysical analyzer for interrogating individual protein sequences (SingleSEQ tab). The latter allows users to correlate regions prone to phase separation with a large array of physicochemical attributes and various short linear motifs. BIAPSS also includes global statistics derived over the universe of most of the known LLPS-driver protein sequences (MultiSEQ tab) for revealing the regularities and sequence-specific signals driving phase separation. Finally, BIAPSS incorporates an extensive cross-reference section that links all entries to primary LLPS databases and other external resources thereby serving as a central navigation hub for the phase separation community. All of the data used by BIAPSS is freely available for download as well-formatted pre-processed data with detailed descriptions, facilitating rapid implementation in user-defined computational protocols. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/430806v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1d90ddorg.highwire.dtl.DTLVardef@111d084org.highwire.dtl.DTLVardef@90b3b0org.highwire.dtl.DTLVardef@51d71_HPS_FORMAT_FIGEXP M_FIG TOC - graphical abstract C_FIG Author summaryProteins, especially those with low complexity and intrinsically disordered regions, have recently come into the limelight because of mounting evidence showing that these regions can drive the formation of membraneless organelles (MLOs) in cells. The underlying physical mechanism for forming MLOs is liquid-liquid phase separation (LLPS); a thermodynamically driven process whereby a cellular milieu with a relatively well-mixed distribution of biomolecules gets decomposed into liquid droplets where the concentration of selected biomolecules is higher. Deciphering molecular sequence grammar of phase separation has turned out to be challenging because of the complexity of this process in cells and the vastness of sequence space of LLPS-driver proteins. While the field is still in its infancy the growth of experimental data has already spurred the creation of several major databases which collect and annotate bimolecular systems with confirmed LLPS behavior. What is currently missing is a framework that would leverage the existing databases by integrating them with deep biophysical and bioinformatic analysis for identifying statistically significant features of protein sequences implicated in LLPS. In this work, we have addressed this challenge by creating an open-source web platform named BIAPSS (BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences) which integrates a comprehensive repository of pre-processed bioinformatic data for LLPS-driver protein sequences with interactive analytic applications for on-the-fly analysis of biophysical features relevant for LLPS behavior. BIAPSS empowers users with novel and effective tools for exploring LLPS-related sequence signals for individual proteins (SingleSEQ tab) and globally by integrating common regularities across subgroups or the entire LLPS sequence superset (MultiSEQ). The long-term plan for BIAPSS is to serve as a unifying hub for the experimental and computational community with a comprehensive set of analytic tools, biophysically featured data, and standardized protocols facilitating the identification of sequence hot spots driving the LLPS, which all can support applications for designing new sequences of biomedical interest.

bioinformatics↗

bioRxiv2021

The carbon footprint of bioinformatics

Bioinformatic research relies on large-scale computational infrastructures which have a non-zero carbon footprint. So far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this study, we estimate the bioinformatic carbon footprint (in kilograms of CO2 equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org). We assess (i) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics and molecular simulations, as well as (ii) computation strategies, such as parallelisation, CPU (central processing unit) vs GPU (graphics processing unit), cloud vs. local computing infrastructure and geography. In particular, for GWAS, we found that biobank-scale analyses emitted substantial kgCO2e and simple software upgrades could make GWAS greener, e.g. upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Switching from the average data centre to a more efficient data centres can reduce carbon footprint by ~34%. Memory over-allocation can be a substantial contributor to an algorithms carbon footprint. The use of faster processors or greater parallelisation reduces run time but can lead to, sometimes substantially, greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimise kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.

bioinformatics↗

bioRxiv2023

Automated Bioinformatics Analysis via AutoBA

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the bioinformatics analysis continues to grow. In response to this need, we introduce Automated Bioinformatics Analysis (AutoBA), an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models. AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. Through rigorous validation by expert bioinformaticians, AutoBAs robustness and adaptability are affirmed across a diverse range of omics analysis cases, including whole genome/exome sequencing (WGS/WES), chromatin immunoprecipitation assays with sequencing (ChIP-seq), RNA sequencing (RNA-seq), single-cell RNA-seq, spatial transcriptomics and so on. AutoBAs unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.

bioinformatics↗

bioRxiv2025

BioMaster: Multi-agent System for Automated Bioinformatics Analysis Workflow

MotivationThe rapid expansion of biological data has significantly increased the complexity of bioinformatics workflows, which often involve intricate, multi-step processes. These tasks demand considerable manual effort from bioinformaticians, creating inefficiencies and limiting scalability. Recent advancements in large language model (LLM)-powered agents offer promising solutions to streamline and automate these workflows. However, existing automated systems, while effective for short, well-defined tasks, often struggle with long, multi-step workflows due to challenges such as error propagation, limited adaptability to emerging tools, and the inability of LLMs to generalize to niche bioinformatics tasks. Achieving effective workflow automation requires robust task coordination, dynamic knowledge retrieval, and mechanisms to ensure errors are identified and resolved before they impact downstream processes. ResultsWe present BioMaster, a multi-agent framework designed to automate and streamline complex bioinformatics workflows. BioMaster incorporates specialized agents with role-based responsibilities, enabling precise task decomposition, execution, and validation. It leverages Retrieval-Augmented Generation (RAG) to dynamically retrieve domain-specific knowledge, improving adaptability to new tools and niche analyses. BioMaster also introduces enhanced control over input and output validation to ensure pipeline consistency and employs a memory management strategy optimized for handling long workflows. Experiments across diverse bioinformatics tasks, including RNA-seq, ChIP-seq, single-cell analysis, and Hi-C processing, demonstrate that BioMaster significantly outperforms existing methods in accuracy, efficiency, and scalability. By addressing key limitations in workflow automation, BioMaster offers a robust solution for modern bioinformatics challenges. Availabilityhttps://github.com/ai4nucleome/BioMaster Contactyanlinzhang@hkust-gz.edu.cn

bioinformatics↗