Our long-term research objective is to understand microtubule organization in living cells, with an emphasis on mitosis. We develop in-vitro assays, quantitative image analysis and cytosim, a computer simulation to study cellular architecture from a mechanistic angle, modeling the interactions of microtubules and related proteins such as molecular motors. In the past, we combined simulations and experiments to study microtubule self-organization, and the mechanical stability of two interacting asters. More recently, we looked at the focusing of mitotic fibers, the formation of antiparallel arrays of microtubules in fission yeast and the spindle positionning in C. elegans.
We are supported by BioMS, an initiative in Systems Biology, and involved in Cell networks.

Link: http://www.cytosim.org

We are looking for a highly motivated candidate with a PhD degree in
Bioinformatics or a related field. Candidates are expected to have a
strong background in evolutionary biology and/or comparative functional
genomics. Additional experiences in single cell functional genomics
analyses, statistics and computational data analyses are a plus, as is
an interest in comparative developmental (EvoDevo) questions.

We offer a dynamic and interactive research environment with state-of-the
art research facilities, good research funding and internationally
competitive salaries.

The Tschopp lab (www.evolution.unibas.ch/tschopp/research/)
studies the gene regulatory mechanisms of cell type
specification and evolution in vertebrates. See also our
preprints at https://doi.org/10.1101/2024.03.26.586769 and
https://doi.org/10.1101/2024.11.28.625862 Applications should include
a motivation letter, a CV, a list of publications, a statement about
research interests, as well as the names and contact details of at
least two referees. Applications (in the form of a single .pdf file)
should be sent to Patrick Tschopp (patrick.tschopp@unibas.ch); review
of applications will begin on January 1st 2025, and will continue until
the position is filled.

Research Area:
Mapping loci in ENU mutants in mice in complex pedigrees
Investigation of DNA sharing in distantly related individuals
CNV analysis in pedigrees and connections to linkage studies
Statistical Genetics

Link @ http://www.wehi.edu.au/faculty_members/dr_melanie_bahlo

Here’s a curated list of the top websites offering bioinformatics job opportunities and postdoctoral fellowships worldwide.

1. General Bioinformatics Job Portals

These platforms are ideal for bioinformaticians seeking jobs in diverse sectors:

• Nature Careers: A trusted resource for job seekers in the sciences, Nature Careers offers bioinformatics roles globally. Their specialized search function allows you to filter jobs by keyword, location, and more.

  • Explore Bioinformatics Jobs on Nature Careers

• Science Careers: A job board from the AAAS, this site focuses on STEM jobs, including numerous bioinformatics opportunities in academia and industry.

• Euraxess: Euraxess is the go-to platform for researchers looking for jobs, fellowships, and funding across Europe and beyond. It lists both bioinformatics roles and research grants.

  • Search Bioinformatics Jobs on Euraxess

• ResearchGate Jobs: ResearchGate is widely known as a platform for researchers to share publications, but it also has a robust job board featuring bioinformatics positions globally.

• FindAPostDoc: This site is dedicated to helping postdoctoral researchers find positions, with bioinformatics being a popular category.

• Academic Positions: Targeting academic roles worldwide, Academic Positions lists bioinformatics jobs at universities and research institutions.

• PostdocJobs.com: Specializing in postdoctoral roles, this platform is a great resource for early-career researchers looking for bioinformatics-related positions.

• Scholarship Positions: In addition to jobs, Scholarship Positions provides information on scholarships, fellowships, and grants related to bioinformatics.

2. Fellowship and Training Opportunities in Bioinformatics

For those seeking fellowships or specialized training, these organizations offer postdoctoral programs, grants, and research opportunities:

• NIH Office of Intramural Training and Education: The National Institutes of Health offer extensive research training programs for postdocs, including those in bioinformatics.

• NSF Research Opportunity Awards: The National Science Foundation funds bioinformatics research at predominantly undergraduate institutions, providing fellowships and grants.

• Top U.S. Universities: Many prestigious U.S. institutions, including Harvard, Berkeley, Yale, MIT, Johns Hopkins, UCSD, and Cornell, offer postdoctoral opportunities in bioinformatics.

3. Country-Specific Job and Fellowship Resources

If you're targeting a specific region, these platforms offer bioinformatics opportunities tailored to their respective countries:

Canada

• CAPS/ACPP: The Canadian Association of Postdoctoral Scholars provides a job board, including bioinformatics roles in academia.
• Banting Postdoctoral Fellowships: A prestigious fellowship program for postdocs in bioinformatics and related fields.
• Mitacs Elevate: A Canadian initiative offering fellowships to connect postdoctoral researchers with industry partners.

United Kingdom

• UKRI: The UK Research and Innovation body funds bioinformatics research and offers various grants.
• The Royal Society: Provides funding schemes for researchers in bioinformatics.
• Marie Skłodowska-Curie Actions: The MSCA funds fellowships and doctoral programs across Europe, including bioinformatics-related projects.
• Wellcome Trust: Offers research funding and career development opportunities in health-related fields, including bioinformatics.

Europe

• EMBO Fellowships: The European Molecular Biology Organization supports bioinformaticians through fellowships and career grants.
• Max Planck Society: A leading research organization offering bioinformatics positions and fellowships across Europe.
• Helmholtz Association: A major research organization in Germany offering bioinformatics roles in various disciplines.
• Leibniz Association: Offers research opportunities, including bioinformatics, across its numerous institutes.

Australia and New Zealand

• Australian Research Council: Offers funding and research schemes, including in bioinformatics.
• Top Universities: Universities like Sydney, Melbourne, and Queensland have research programs in bioinformatics.

Asia

• Japan Society for the Promotion of Science (JSPS): Offers fellowships for international researchers in bioinformatics.
• Top Institutions: Universities like NUS, CAS, and IISc are leading hubs for bioinformatics research.

Middle East

• Qatar Research, Development, and Innovation (QRDI): Offers research opportunities in bioinformatics.
• KAUST: A leading university in Saudi Arabia offering bioinformatics research positions.

Africa

• African Academy of Sciences: Provides career opportunities and research funding in bioinformatics across Africa.

Conclusion

The field of bioinformatics is full of exciting opportunities for those with the right skills. Whether you are looking for a postdoc position, research funding, or a long-term job in industry, these platforms are an excellent starting point. Explore, apply, and take the next step in your bioinformatics career!

Genome analysis, genome visualization, mutation detection, molecular docking, comparative genomics, cancer informatics

Link @ http://www.bcgsc.ca

"The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information."

"The collection, organization and analysis of large amounts of biological data, using networks of computers and databases." - from the glossary for ABC Science Online's feature: The State of the Genome 2001.

"It is defined here as an interdisciplinary research area that applies computer and information science to solve biological problems. However, this is not the only definition. The field is being defined (and redefined) at present, and there are probably as many definitions as there are bioinformaticians (bioinformaticists?).

The following references are a snapshot of the moving target named bioinformatics. ... " - from the University of Minnesota Graduate Program in Bioinformatics' page: What is Bioinformatics,

"The application of computer technology to the management of biological information.Bioinformatics uses computers to solve problems in the life sciences, such as determination of DNA and protein sequences, investigation of protein functions, development of pharmaceuticals. It involves the creation of extensive electronic databases on genomes and protein sequences, and techniques such as the three-dimensional modeling of biomolecules and biologic systems. ..." - from the Bioinformatics Glossary edited by Charles E. Kahn, Jr., Medical College of Wisconsin.

"Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned." - from the National Center for Biotechnology Information's Bioinformatics Factsheet.

"Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data." - NIH Bioinformatics Web site

"The use of computers, laboratory robots and software to create, manage and interpret massive sets of complex biological data." - from the glossary for the University of Michigan Health System's Symphony of Life: Genetics & Medicine Web site.

"The field of science in which biology, computer science, and information technology merge into a single discipline.There are three important sub-disciplines within bioinformatics: (1) the development of new algorithms and statistics with which to assess relationships among members of large data sets; (2) the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and (3) the development and implementation of tools that enable efficient access and management of different types of information." - U.S. Environmental Protection Agency's ComputationalToxicology Research Glossary.

What is Bioinformatics? "One idea for a definition: (Molecular) Bio - informatics = is conceptualizing biology in terms of molecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied math, CS, and statistics) to understand and organize the information associated with these molecules, on a large-scale." - By Mark Gerstein, Gerstein Group - Yale Bioinformatics.

Bioinformatics

Definition:

Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine.

Description:

The history of computing in biology goes back to the 1920s when scientists were already thinking of establishing biological laws solely from data analysis by induction (e.g. A.J. Lotka, Elements of Physical Biology, 1925). However, only the development of powerful computers, and the availability of experimental data that can be readily treated by computation (for example, DNA or amino acid sequences and three–dimensional structures of proteins) launched bioinformatics as an independent field. Today, practical applications of bioinformatics are readily available through the world wide web, and are widely used in biological and medical research. As the field is rapidly evolving, the very definition of bioinformatics is still the matter of some debate.

The relationship between computer science and biology is a natural one for several reasons. First, the phenomenal rate of biological data being produced provides challenges: massive amounts of data have to be stored, analysed, and made accessible. Second, the nature of the data is often such that a statistical method, and hence computation, is necessary. This applies in particular to the information on the building plans of proteins and of the temporal and spatial organisation of their expression in the cell encoded by the DNA. Third, there is a strong analogy between the DNA sequence and a computer program (it can be shown that the DNA represents a Turing Machine).

Analyses in bioinformatics focus on three types of datasets: genome sequences, macromolecular structures, and functional genomics experiments (e.g. expression data, yeast two–hybrid screens). But bioinformatic analysis is also applied to various other data, e.g. taxonomy trees, relationship data from metabolic pathways, the text of scientific papers, and patient statistics. A large range of techniques are used, including primary sequence alignment, protein 3D structure alignment, phylogenetic tree construction, prediction and classification of protein structure, prediction of RNA structure, prediction of protein function, and expression data clustering. Algorithmic development is an important part of bioinformatics, and techniques and algorithms were specifically developed for the analysis of biological data (e.g., the dynamic programming algorithm for sequence alignment).

Bioinformatics has a large impact on biological research. Giant research projects such as the human genome project [4] would be meaningless without the bioinformatics component. The goal of sequencing projects, for example, is not to corroborate or refute a hypothesis, but to provide raw data for later analysis. Once the raw data are available, hypotheses may be formulated and tested in silico. In this manner, computer experiments may answer biological questions which cannot be tackled by traditional approaches. This has led to the founding of dedicated bioinformatics research groups as well as to a different work practice in the average bioscience laboratory where the computer has become an essential research tool.

Three key areas are the organisation of knowledge in databases, sequence analysis, and structural bioinformatics.

Organizing biological knowledge in databases:

Biological raw data are stored in public databanks (such as Genbank or EMBL for primary DNA sequences). The data can be submitted and accessed via the world wide web. Protein sequence databanks like trEMBL provide the most likely translation of all coding sequences in the EMBL databank. Sequence data are prominent, but also other data are stored, e. g. yeast two–hybrid screens, expression arrays, systematic gene–knock–out experiments, and metabolic pathways.

The stored data need to be accessed in a meaningful way, and often contents of several databanks or databases have to be accessed simultaneously and correlated with each other. Special languages have been developed to facilitate this task (such as the Sequence Retrieval System (SRS) and the Entrez system). An unsolved problem is the optimal design of inter–operating database systems. Databases provide additional functionality such as access to sequence homology searches and links to other databases and analysis results. For example, SWISSPROT [1] contains verified protein sequences and more annotations describing the function of a protein. Protein 3D structures are stored in specific databases (for example, the Protein Data Bank [2], now primarily curated and developed by the Research Collaboratory for Structural Bioinformatics). Organism specific databases have been developed (such as ACEDB, the A C. Elegans DataBase for the C. elegans genome, FLYBASE for D. melanogaster etc). A major problem are errors in databanks and databases (mostly errors in annotation), in particular since errors propagate easily through links.

Also databases of scientific literature (such as PUBMED, MEDLINE) provide additional functionality, e.g. they can search for similar articles based on word–usage analysis. Text recognition systems are being developed that extract automatically knowledge about protein function from the abstracts of scientific articles, notably on protein–protein interactions.

Analysing sequence data:

The primary data of sequencing projects are DNA sequences. These become only really valuable through their annotation. Several layers of analysis with bioinformatics tools are necessary to arrive from a raw DNA sequence at an annotated protein sequences:

• establish the correct order of sequence contigs to obtain one continuous sequence;
• find the tranlation and transcription initiation sites, find promoter sites, define open reading frames (ORF);
• find splice sites, introns, exons;
• translate the DNA sequence into a protein sequence, searching all six frames;
• compare the DNA sequence to known protein sequences in order to verify exons etc with homologuous sequences.

Some completely automated annotation systems have been developed (e.g., GENEQUIZ), which use a multitude of different programs and methods.

The protein sequences are further analysed to predict function. The function can often be inferred if a sequence of a homologous protein with known function can be found. Homology searches are the predominant bioinformatics application, and very efficient search methods have been developed [3]. The often difficult distinction between orthologous sequences and paralogous sequences facilitates the functional annotation in the comparison of whole genomes. Several methods detect glycolysation, myristylation and other sites, and the prediction of signal peptides in the amino acid sequence give valuable information about the subcellular location of a protein.

The ultimate goal of sequence annotation is to arrive at a complete functional description of all genes of an organism. However, function is an ill–defined concept. Thus, the simplified idea of “one gene – one protein – one structure – one function” cannot take into account proteins that have multiple functions depending on context (e.g., subcellar location and the presence of cofactors). Well-known cases of “moonlighting” proteins are lens crystalline and phosphoglucose isomerase. Currently, work on ontologies is under way to explicitly define a vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing.

Families of similar sequences contain information on sequence evolution in the form of specific conservation patters at all sequence positions. Multiple sequence alignments are useful for

• building sequence profiles or Hidden Markov Models to perform more sensitive homology searches. A sequence profile contains information about the variability of every sequence position. improving structure prediction methods (secondary structure prediction). Sequence profile searches have become readily available through the introduction of PsiBLAST [3];
• studying evolutionary aspects, by the construction of phylogenetic trees from the pairwise differences between sequences: for example, the classification with 70S, 30S RNAs established the separate kingdom of archeae;
• determining active site residues, and residues specifc for subfamilies;
• predicting protein–protein interactions;
• analysing single nucleotide polymorphisms to hunt for genetic sources of deseases.
• Many complete genomes of microorganisms and a few of eukaryotes are available [4]. By analysis of entire genome sequences a wealth of additional information can be obtained. The complete genomic sequence contains not only all protein sequences but also sequences regulating gene expression. A comparison of the genomes of genetically close organisms reveals genes responsible for specific properties of the organisms (e.g., infectivity). Protein interactions can be predicted from conservation of gene order or operon organisation in different genomes. Also the detection of gene fusion and gene fission (i.e, one protein is split into two in another genome) events helps to deduce protein interactions.

Structural bioinformatics:

This branch of bioinformatics is concerned with computational approaches to predict and analyse the spatial structure of proteins and nucleic acids. Whereas in many cases the primary sequence uniquely specifies the three–dimensional (3D) structure, the specific rules are not well understood, and the protein folding problem remains largely unsolved. Some aspects of protein structure can already be predicted from amino acid content. Secondary structure can be deduced from the primary sequence with statistics or neural networks. When using a multiple sequence alignment, secondary structure can be predicted with an accuracy above 70 %.

3D models can be obtained most easily if the 3D structure of a homologous protein is known (homology modelling, comparative modelling). A homology model can only be as good as the sequence alignment: whereas protein relationships can be detected at the 20% identity level and below, a correct sequence alignment becomes very difficult, and the homology model will be doubtful. From 40 to 50% identity the models are usually mostly correct; however, it is possible to have 50% identity between two carefully designed protein sequences with different topology (the so –called JANUS protein). Remote relationships that are undetectable by sequence comparisons may be detected by sequence–to–structure–fitness (or threading) approaches: the search sequence is systematically compared to all known protein structures. Ab initio predictions of protein 3D structure remains the major challenge; some progress has been made recently by combining statistical with force–field based approaches.

Membrane proteins are interesting drug targets. It is estimated that membrane receptors form 50 % of all drug targets in pharmacological research. However, membrane proteins are underrepresented in the PDB structure database. Since membrane proteins are usually excluded from structural genomics initiatives due to technical problems, the prediction of transmembrane helices and solvent accessibility is very important. Modern methods can predict transmembrane helices with a reliability greater than 70 %.

Understanding the 3D structure of a macromolecule is crucial for understanding its function. Many properties of the 3D structure cannot be deduced directly from the primary sequence. Obtaining better understanding of protein function is the driving force behind structural genomics efforts, which can be thus understood as part of functional genomics. Similar structure can imply similar function. General structure–to–function relationships can be obtained by statistical approaches, for example, by relating secondary structure to known protein function or surface properties to cell location.

The increased speed of structure determination necessary for the structural genomics projects make an independent validation of the structures (by comparison to expected properties) particularly important. Structure validation helps to correct obvious errors (e.g., in the covalent structure) and leads to a more standardized representation of structural data, e.g., by agreeing on a common atom name nomenclature. The knowledge of the structure quality is a prerequisite for further use of the structure, e.g in molecular modelling or drug design.

In order to make as much data on the structure and its determination available in the databases, approaches for automated data harvesting are being developed. Structure classification schemes, as implemented for example in the SCOP, CATH, and FSSP databases, elucidate the relationship between protein folds and function and shed light on the evolution of protein domains.

Combined analysis of structural and genomic data will certainly get more important in the near future. Protein folds can be analysed for whole genomes. Protein–protein interactions predicted on the sequence level, can be studied in more detail on the structure level. Single Nucleotide Polymorphisms can be mapped on 3D structures of proteins in order to elucidate specific structural causes of disease.

More detailed aspects of protein function can be obtained also by force–field based approaches. Whereas protein function requires protein dynamics, no experimental technique can observe it directly on an atomic scale, and motions have to be simulated by molecular dynamics (MD) simulations. Also free energy differences (for example between binding energies of different protein ligands) can be characterized by MD simulations. Molecular mechanics or molecular dynamics based approaches are also necessary for homology modelling and for structure refinement in X–ray crystallography and NMR structure determination.

Drug design exploits the knowledge of the 3D structure of the binding site (or the structure of the complex with a ligand) to construct potential drugs, for example inhibitors of viral proteins or RNA. In addition to the 3D structure, a force field is necessary to evaluate the interaction between the protein and a ligand (to predict binding energies). In virtual screening, a library of molecules is tested on the computer for their capacities to bind to the macromolecule.

Pharmacological Relevance:

Many aspects of bioinformatics are relevant for pharmacology. Drug targets in infectious organisms can be revealed by whole genome comparisons of infectious and non–infectious organisms. The analysis of single nucleotide polymorphisms reveals genes potentially responsible for genetic deseases. Prediction and analysis of protein 3D structure is used to develop drugs and understand drug resistance.

Patient databases with genetic profiles, e.g. for cardiovascular diseases, diabetes, cancer, etc. may play an important role in the future for individual health care, by integrating personal genetic profile into diagnosis, despite obvious ethical problems. The goal is to analyse a patient’s individual genetic profile and compare it with a collection of reference profiles and other related information. This may improve individual diagnosis, prophylaxis, and therapy.

References:

Bairoch A, Apweiler R (2000) The SWISS–PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28:45–48
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res. 28:235–42
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI–BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:3389–3402
Pearson WR (2000) Flexible sequence similarity searching with the FASTA3 program package. Methods Mol. Biol. 132:185–219
The Genome International Sequencing Consortium (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921
JC Venter et al. (2001) The sequence of the human genome. Science 291:1304–1351
R.D. Fleischmann et al. (1995) Whole–genome random sequencing and assembly of haemophilus–influenzae. Science 269:496–51

1. Big Data and Bioinformatics

The exponential growth in biological data, driven by advancements in sequencing technologies and high-throughput experiments, has made bioinformatics an indispensable tool. By 2030, we anticipate:

• Petabyte-Scale Data Management: Enhanced storage solutions and cloud computing platforms will allow researchers to handle the vast amounts of data generated from omics studies, including genomics, transcriptomics, and proteomics.

• AI and Machine Learning Integration: Sophisticated algorithms will uncover patterns and relationships in large datasets, enabling predictions about gene function, disease susceptibility, and therapeutic outcomes.

2. Personalized Medicine and Genomics

Bioinformatics will play a pivotal role in tailoring healthcare to individual patients. Key developments include:

• Whole-Genome Sequencing in Clinics: The decreasing cost of sequencing will make it routine in medical diagnostics, enabling personalized treatment plans based on an individual’s genetic makeup.

• Drug Repurposing and Development: Computational tools will identify potential new uses for existing drugs, accelerating the development of targeted therapies.

3. Advancing Computational Tools

The future will see the development of more user-friendly and powerful bioinformatics tools:

• Graph-Based Approaches: Enhanced algorithms for analyzing complex biological networks, such as protein-protein interaction maps.

• Visualization Tools: Intuitive software for visualizing multi-dimensional data, enabling researchers to interpret findings more effectively.

4. Synthetic Biology and Systems Biology

Bioinformatics will continue to drive progress in synthetic and systems biology by:

• Gene Circuit Design: Leveraging computational models to design and simulate synthetic biological systems.

• Understanding Cellular Pathways: Integrating multi-omics data to model cellular processes with unprecedented accuracy.

5. Bioinformatics in Agriculture and Environmental Science

Beyond healthcare, bioinformatics will revolutionize agriculture and environmental conservation:

• Crop Improvement: Genomic studies will help develop high-yield, disease-resistant, and climate-resilient crops.

• Microbial Ecology: Metagenomics will enhance our understanding of microbial communities, aiding in bioremediation and ecosystem management.

6. Democratization of Bioinformatics

Open-source software and accessible education will broaden participation in bioinformatics research:

• Community-Driven Projects: Collaborative platforms like GitHub will continue to foster innovation in tool development.

• Education and Training: Online courses and workshops will bridge skill gaps, enabling researchers from diverse backgrounds to contribute.

Challenges and Ethical Considerations

While the future is bright, challenges remain. Data privacy and ethical concerns surrounding genetic information require careful navigation. Furthermore, addressing the digital divide is critical to ensuring equitable access to bioinformatics resources globally.

Conclusion

The future of bioinformatics is boundless, with opportunities to revolutionize our understanding of life and improve human health. As technologies evolve and collaborations flourish, bioinformatics will undoubtedly remain at the forefront of scientific discovery, unlocking the secrets of life one dataset at a time.

*The journals are not listed in any ascending, descending, or impact factors oders. 

Bioinformatics 

Journal of Computational Biology

Briefings in Bioinformatics

In Silico Biology

Structure

Protein Science

Protein Engineering

Proteomics

Nucleic Acids Research

Trends in Biotechnology

Proceedings of the National Academy of Sciences

Folding and Design

Genome Biology

Journal of Biomedical Informatics

Bioinformation

Journal of Computational Intelligence in Bioinformatics

Journal of Structural and Functional Genomics

Journal of Molecular Graphics and Modelling

Metabolic Engineering

Computers & Chemistry

Artificial Intelligence in Medicine

Journal of Biomedical Science

Artificial Intelligence

Machine Learning

Applied Bioinformatics

Applied Genomics and Proteomics

BMC Bioinformatics

Online Journal of Bioinformatics (OJB)

PSB On-Line Proceedings

Bioinformatics: Information Technology & Systems (BITS)

Data Mining and Knowledge Discovery

The EMBO Journal

Current Opinions in Structural Biology

Journal of Molecular Microbiology and Biotechnology

Nature

Nature Structural Biology

Journal of Machine Learning Research

Nature Genetics

Current Opinion in Genetics & Development

Nature Biotechnology

Trends in Biochemical Sciences

Proteins: Structure, Function, and Genetics

Nature Cell Biology

Trends in Genetics

ChemBioChem

Trends in Molecular Medicine

Journal of Molecular Modelling

Trends in Pharmacological Sciences

Drug Discovery Today

Others Free Online Full-text Journals

1️⃣ Data Analysis for the Life Sciences
A fantastic starting point to learn statistics, R programming, and exploratory data analysis in the context of biology. The best part? It’s available free online from HarvardX.

2️⃣ Practical Computing for Biologists
The very first book I picked up when I started learning computational biology. It’s beginner-friendly and focuses on essential computing skills every biologist needs.

3️⃣ A Primer for Computational Biology
An open-access, hands-on introduction to computational biology concepts and coding techniques. Perfect if you want to learn through real examples.

4️⃣ Computational Genomics with R
For those who already know R and want to dive deeper into genome-scale data analysis, from sequence alignment to gene expression.

5️⃣ The Biologist’s Guide to Computing
Bridges the gap between biological problems and computational thinking, making it easier for life scientists to approach programming and data analysis.

6️⃣ Bioinformatics Data Skills
A must-read to sharpen your bioinformatics toolkit — from command-line skills to reproducible research workflows. Ideal once you’ve covered the basics.

7️⃣ Bioinformatics Workbook
A practical tutorial series to help scientists design bioinformatics projects, analyze data, and understand best practices.

8️⃣ Modern Statistics for Modern Biology
An essential guide to modern statistical methods applied to biology, blending theory with hands-on examples in R.

9️⃣ Algorithms on Strings, Trees, and Sequences by Dan Gusfield
A classic reference for anyone wanting to understand the algorithms behind sequence alignment, genome assembly, and biological data structures.