What is RNA-Seq?

RNA-Seq is a next-generation sequencing (NGS) technology used to study the transcriptome—the complete set of RNA molecules in a cell. It quantifies gene expression, detects novel transcripts, and captures alternative splicing events with high sensitivity and resolution.

Workflow for RNA-Seq Analysis

RNA-Seq analysis involves several stages, each requiring computational tools and expertise.

1. Experimental Design and Data Acquisition

Before diving into analysis, bioinformaticians should consider:

• Biological Replicates: Ensure statistical power to detect meaningful differences.
• Sequencing Depth: Align sequencing depth to study objectives (e.g., higher depth for low-abundance transcripts).
• Paired-End vs. Single-End: Paired-end sequencing provides more detailed information on transcript structure.

Once sequencing is complete, raw data is provided in FASTQ format, containing sequence reads and quality scores.

2. Quality Control and Preprocessing

Quality control (QC) ensures data integrity. Tools such as FastQC evaluate metrics like base quality, GC content, and adapter contamination.

Preprocessing Steps:

• Trimming: Tools like Trimmomatic or Cutadapt remove low-quality bases and adapter sequences.
• Filtering: Discard reads below a certain quality threshold or length.

3. Read Alignment

Reads are mapped to a reference genome or transcriptome to determine their origin. Alignment tools include:

• HISAT2: Handles large genomes efficiently and supports spliced alignments.
• STAR: High-speed aligner optimized for RNA-Seq.
• Bowtie2: Suitable for short-read alignment.

Output: A SAM/BAM file containing aligned reads.

4. Transcript Assembly and Quantification

This step involves identifying transcripts and quantifying their expression levels. Tools used include:

• StringTie: Assembles and quantifies transcripts from aligned reads.
• Salmon/Kallisto: Perform pseudo-alignment for rapid and accurate quantification.

Expression levels are typically measured as TPM (transcripts per million) or FPKM (fragments per kilobase of transcript per million mapped reads).

5. Differential Expression Analysis

To identify genes with altered expression between conditions, bioinformaticians use tools such as:

• DESeq2: Accounts for data normalization and variability.
• edgeR: Handles overdispersed count data efficiently.
• Limma-voom: Combines linear modeling with RNA-Seq count data.

The output includes a list of differentially expressed genes (DEGs) with statistical significance and fold-change values.

6. Functional Annotation and Pathway Analysis

Understanding the biological significance of DEGs involves:

• Gene Ontology (GO) Analysis: Tools like DAVID or clusterProfiler categorize genes based on their biological functions.
• Pathway Enrichment Analysis: Identifies pathways enriched in DEGs using tools like KEGG, Reactome, or GSEA.

7. Visualization

Visualizing results enhances interpretability. Common visualizations include:

• Heatmaps: Show expression patterns across samples (e.g., pheatmap).
• Volcano Plots: Highlight significant DEGs (e.g., ggplot2).
• PCA/UMAP: Assess sample clustering and variability (e.g., Seurat).

Challenges in RNA-Seq Analysis

1. Batch Effects: Technical variability can confound biological signals. Combat this with normalization techniques or batch-correction tools like ComBat.
2. Low-Quality Samples: Poor-quality RNA impacts downstream analyses.
3. Computational Complexity: RNA-Seq generates massive datasets, requiring robust computing resources and optimized pipelines.

Key Tools and Resources

• Bioconductor: A treasure trove of R packages for RNA-Seq analysis.
• Galaxy: A web-based platform for running RNA-Seq workflows.
• Nextflow/Snakemake: Workflow management tools to streamline analyses.

Applications of RNA-Seq

RNA-Seq is used in diverse research areas, including:

• Cancer Transcriptomics: Identifying tumor-specific expression profiles.
• Developmental Biology: Studying dynamic transcriptome changes.
• Drug Discovery: Screening genes modulated by therapeutic compounds.

Conclusion

RNA-Seq analysis is a cornerstone of modern transcriptomics, offering bioinformaticians a versatile toolkit for unraveling gene expression and regulation. Mastering RNA-Seq workflows and tools empowers researchers to transform raw sequencing data into biological discoveries.

Whether you’re investigating disease mechanisms, exploring cellular pathways, or developing new therapeutics, RNA-Seq is a powerful ally in your bioinformatics arsenal.

More @ http://www.bioinf.manchester.ac.uk/dbbrowser/index.php

National Bioinformatics Infrastructure Sweden (NBIS) (nbis.se) plays an important role in advancing life science research in Sweden by providing expert support and developing cutting-edge bioinformatics infrastructure. Operating as a truly national initiative, NBIS employs more than 120 bioinformaticians, system developers, and data stewards across multiple locations in Sweden. It serves as the bioinformatics platform at SciLifeLab, a national resource that facilitates research in molecular biosciences by offering access to state-of-the-art technologies and technical expertise. With strong ties to data-producing facilities and ongoing collaborations with leading research groups, NBIS is ideally positioned to support world-class bioinformatics analyses. Furthermore, NBIS is the Swedish node in ELIXIR, the European infrastructure for biological information.

NBIS is seeking an experienced bioinformatician to support both Swedish and international projects. As part of our dynamic team, you will work closely with researchers to process large-scale biological data and contribute to advancing our data analysis infrastructure. Strong problem-solving skills, attention to detail, and the ability to troubleshoot complex bioinformatics pipelines are essential for success in this role. Flexibility and a willingness to learn are also important, as NBIS continually adapts to meet the evolving needs of the Swedish research community.

More at https://www.uu.se/en/about-uu/join-us/jobs-and-vacancies/job-details?query=778701

No. ACTREC/Advt./ 72 /2013

WALK IN INTERVIEW

1. JRF*
Genome-wide RNAi screen with human pooled tyrosine kinase shRNA libraries in head and neck squamous call carcinoma (HNSCC) cell lines
DBT A/C No. 3071, Dr. Amit Dutt

2. JRF
IRB Project ACTREC Funds
Dr. Amit Dutt

3. RA
Defining the cancer genome of Head and Neck Squamous Cell Carcinoma (HNSCC) with SNP arrays and next generation sequencing technology
A/C No. 2895, Dr. Amit Dutt

Duration of the Project: One year from the date of appointment, or as and when project terminates.

Consolidated Salary: RA : Rs. 40,000/- p.m.
JRF* (DBT): Rs. 20,800/- p.m.
JRF: Rs. 16,000/- p.m.
Date & Time: 6th November, 2013, at 10.00 a.m.

Venue: Conference Room

Minimum Qualifications and Experience:

RA: The ideal applicant should have a PhD in a relevant field. He/she should have a strong computational biology background, with demonstrated experience in coding using Perl, Python, Java or C++. He/she should be familiar with working in unix enviromnent, devising computational algorithms for data analysis, statistical data analysis in R and matlab and database programming using MySQL. Hands on experience in analyzing high throughput data would be an added advantage.

JRF* (DBT project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Valid UGC-CSIR/DBT/ICMR JRF qualification and laboratory experience in molecular biology. Previous experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team would be desirable.

JRF (ACTREC project): M.Sc. in Life Sciences or M.Tech in Biotechnology with good academic record (Minimum of 60% aggregate). Minimum 2 yrs experience in molecular biology and animal tissue culture with high throughput platforms and ability to work with a large team is essential.

*M.Sc. degree obtained after a one year course will not be considered.

Candidates fulfilling above requirements should send their application by e-mail to
‘careers.duttlab@gmail.com. in the format given below so as to reach on or before
4th November, 2013.

Advertisement:

http://www.actrec.gov.in/data%20files/2013/AD-RA-JR-TECHN-6-NOV.pdf

1. Project Junior Research Fellow / Project Assistant

Last Date: 11th Nov 2013

Qualification B.Tech (Comp. Sci.), B.Tech/M.Tech (Bioinformatics), MCA, M.Sc. (Mathematics/Statistics)

Desirable Qualifications: Programming in FORTRAN/ C /PERL, Web application technologies

Upper Age limit 28

Rs.12000 / Rs.16000 (as sanctioned by the funding agency)

General terms and conditions:

Positions are purely temporary and co-terminus with the project.

HRDG (CSIR) prevailing guidelines are applicable these positions.

All categories of applicants are required to submit online application.

Enhancement of stipend to Project JRF to Project SRF will be with the due recommendation of Principal Investigator and approval of the Director on the evaluation of the 3 member Standing Committee consisting of Chairperson at the level of Chief Scientist, Coordinator of the JRFs/RAs/PDFs and the Principal Investigator of the Project.

The age relaxation as per HRDG (CSIR) norms: SC/ST/OBC/Women/Physically Handicapped persons – five years.

The Stipend normally be fixed at Rs.22000/- for Research Associates/Post Doc. Fellows. However, a selected RA/PDF may be placed in the higher start of stipend if there is ample justification and such recommendation is made by the Selection Committee. Based on the recommendation with justification by the PI and approval of the Director, person getting stipend at lower rate may be elevated to higher rate subject to availability of the funds in the project.

Recruitment will be based on initial screening based on qualifications and experience criteria and also based on suitability of the candidates to the nature of research project. This screening will be followed by written test followed / interview. After completing this process, candidates will be shortlisted and appointed in specific project subjects as and when appropriate positions become available. The pool of selected candidates will be valid for six months.

Remunerations indicate are maximum admissible and will depend upon the availability of funds and subject to conditions applicable to projects from different funding agencies at the time of recruitment.

Apply : http://www.ccmb.res.in/positions/projects/temp_positions.php

Form download : http://www.ccmb.res.in/positions/projects/oct-2013/pdf_download.php

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.

Lab page: http://www.oeb.harvard.edu/faculty/edwards/index.html

People using computers were often seen
as helpers, not leaders—
useful, but not essential.

Sometimes, the criticism was direct.
Sometimes subtle.
But the message was the same—
this work doesn’t really count.

Then biology changed.
The questions became bigger,
and experiments alone
were no longer enough.

Organizing knowledge by hand worked once.
Now it needs computers—
to handle scale, speed, and complexity.

Some patterns are simply invisible
if you look at one sample.
You need many—
and the right tools to understand them.

So we started building maps—
of genomes, cells, and systems.
Not perfect,
but extremely useful.

Ideas also had to become clearer.
It’s no longer enough to say something sounds right—
you have to measure it.

The divide between “types” of biologists
never really made sense.
We are solving the same problems—
just in different ways.

Progress didn’t wait for agreement.
It moved forward with data,
with code,
and with careful analysis.

What matters now is simple:
• Biology depends on computation
• Coding is an important skill
• Statistics helps us think clearly
• And the people building these tools
are shaping the future of science

Last Date for Submitting Application: 25th November 2013

1. Senior Scientist: (01 position)
Pay Scale: Rs.40, 000/-
Qualifications: PhD/ Post Doctoral with Experience in CADD

2. Junior Scientist (10 positions)
Pay Scale: Rs. 22,000/-
Qualifications: MPhil / Masters Degree in Bioinformatics / Computational Biology / CADD / Ayurveda

3. Technical Assistant (01+01 positions)
Pay Scale: Rs.12,000/-
Qualifications: 1. BSc Computer Science/ MCA
Qualifications: 2. MSc Biotechnology / MSc Microbiology

4. Programmer (01 position)
Pay Scale: Rs.20,000/-
Qualifications: MSc Computer Science/ MCA / B Tech (Experience in MATLAB, C, C++) Industrial experience is desirable

5. Teaching Assistant (03 positions)
Pay Scale: Rs.10,000/-
Qualifications: MSc in Bioinformatics

6. Administration Assistant (02 positions)
Pay Scale: Rs.8,000/-
Qualifications: Degree + PGDCA

The Selection process comprises of written test and interview. Positions are purely temporary (initially for the period of one year) and co-terminus with the project. For more details mail to: cbi.uok [at] gmail.com

More detail @ https://sites.google.com/site/centreforbioinformatics/announcements/applicationsinvitedforapplicationforai-caddproject

Sequence Analysis and General Bioinformatics

• BLAST, Ian Korf, Mark Yandell, Joseph Bedell, 2003, O'Reilly
• Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases, Scott Markel, Darryl Leon, 2003, O'Reilly
• Bioinformatics for Geneticists, Michael Barnes, Ian C Gray (Editors), 2003, John Wiley & Sons
• Bioinformatics for Dummies, Jean-Michel Claverie, Cedric Notredame, 2003, John Wiley & Sons
• Mathematics of Genome Analysis, Jerome K. Percus, 2002, Cambridge Univ Press
• Bioinformatics Computing, Bryan P. Bergeron, 2002, Prentice Hall
• Evolutionary Computation in Bioinformatics, Gary B. Fogel, David W. Corne (Editors), 2002, Morgan Kaufmann
• Introduction to Bioinformatics, Arthur M. Lesk, 2002, Oxford University Press
• Instant Notes in Bioinformatics, D.R. Westhead, J. H. Parish, R.M. Twyman, 2002, Bios Scientific Pub
• Fundamental Concepts of Bioinformatics, Dan E. Krane, Michael L. Raymer, Michaeel L. Raymer, Elaine Nicpon Marieb, 2002, Benjamin/Cummings
• Essentials of Genomics and Bioinformatics, C. W. Sensen (Editor), 2002, John Wiley & Sons
• Current Topics in Computational Molecular Biology (Computational Molecular Biology), Tao Jiang, Ying Xu, Michael Zhang (Editors), 2002, MIT Press
• Hidden Markov Models for Bioinformatics, Timo Koski, Timo Koskinen, 2001, Kluwer Academic Publishers
• Bioinformatics: From Genomes to Drugs, Thomas Lengauer (Editor), 2001, John Wiley & Sons
• Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health), Warren Ewens, Gregory Grant, 2001, Springer Verlag
• Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition, Andreas D. Baxevanis, B. F. Francis Ouellette, 2001, Wiley-Interscience
• Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning), Pierre Baldi, Soren Brunak, Sren Brunak, 2001, MIT Press
• Introduction to Bioinformatics, T eresa Attwood, David Parry-Smith, 2001, Prentice Hall
• Bioinformatics: A Primer, Charles Staben, 2001, Jones & Bartlett Pub
• Data Analysis and Classification for Bioinformatics, Arun Jagota, 2000, AKJ Academics
• Bioinformatics: Sequence and Genome Analysis, David W. Mount, 2001, Cold Spring Harbor Laboratory Press
• Bioinformatics: A Biologist's Guide to Biocomputing and the Internet, Stuart M. Brown, 2000, Eaton Pub Co
• Bioinformatics: Sequence, Structure and Databanks: A Practical Approach (The Practical Approach Series, 236), Des Higgins (Editor), Willie Taylor (Editor), 2000, Oxford Univ Press
• Neural Networks and Genome Informatics, Cathy H. Wu, Jerry W. McLarty, 2000, Elsevier Science
• Computational Molecular Biology: An Introduction (Wiley Series in Mathematical and Computational Biology), Peter Clote and Rolf Backofen, 2000, John Wiley & Sons
• Computational Molecular Biology: An Algorithmic Approach, Pavel A. Pevzner, 2000, MIT Press
• Post-Genome Informatics, Minoru Kanehisa, 2000, Oxford Univ Press
• Mathematical and Computational Biology: Computational Morphogenesis, Hierarchical Complexity, and Digital Evolution, Chrystopher L. Nehaniv, 1999, American Mathematical Society
• Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications, Jason T. L. Wang, Bruce A. Shapiro, Dennis Elliott Shasha (Editors), 1999, Oxford Univ Press
• Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, David Sankoff and Joseph Kruskal (Editors), 1999, Cambridge University Press
• Bioinformatics Basics: Applications in Biological Science and Medicine, Hooman Rashidi, 1999, CRC Press
• Bioinformatics: Methods and Protocols (Methods in Molecular Biology, Vol 132), Stephen Misener and Stephen A. Krawetz (Editors),1999, Humana Press
• Bioinformatics: Databases and Systems, Stanley Letovsky (Editor),1999, Kluwer Academic Publishers
• Computational Molecular Biology, P. Green, 1998, Blackwell Science Inc.
• Computational Methods in Molecular Biology (New Comprehensive Biochemistry, V. 32), Steven L. Salzberg, David B. Searls, Simon Kasif (Editors), 1998, Elsevier Science Ltd.
• Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Richard Durbin, S. Eddy, A. Krogh, G. Mitchison, 1998, Cambridge University Press
• Guide to Human Genome Computing, M. J. Bishop (Editor), 1998, Academic Press
• Introduction to Computational Molecular Biology, Joao Meidanis, Joao C. Setabal, 1997, PWS Pub. Co.
• Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Dan Gusfield, 1997, Cambridge University Press
• Sequence Data Analysis Guidebook, Simon R. Swindell (Editor), 1997, Humana Press
• High Performance Computational Methods for Biological Sequence Analysis, Tieng K. Yap, Ophir Frieder, Robert L. Martino, 1996, Kluwer Academic Pub.
• Computer Methods for Macromolecular Sequence Analysis, Methods in Enzymology, volume 266, Russell F. Doolittle (Editor), 1996, Academic Press
• DNA and Protein Sequence Analysis: A Practical Approach (Practical Approach Series , No 171), 1996, M. J. Bishop and C. J. Rawlings (Editors), 1996, IRL Press
• Molecular Bioinformatics: Algorithms and Applications, Steffen Schulze-Kremer, 1995, Walter De Gruyter
• Introduction to Computational Biology - Maps, sequences and genomes, Michael S. Waterman, 1995, Chapman & Hall
• Computer Analysis of Sequence Data, Annette M. Griffin and Hugh G. Griffin (Editors), 1994, Humana Press
• Artificial Intelligence and Molecular Biology, Lawrence Hunter (Editor), 1993, AAAI Press
• Sequence Analysis Primer, Michael Gribskov and John Devereux (Editors), 1992, Oxford University Press
• Mathematical Methods of Analysis of Biopolymer Sequences (Dimacs Series in Discrete Mathematics and Theoretical Computer Science ; Volume 8), S. G. Gindikin, 1992, American Mathematical Society
• Mathematical Methods for DNA Sequences, Michael S. Waterman (Editor), 1989, CRC Press

Programming Books for Bioinformatics

• Mastering Perl for Bioinformatics, James D. Tisdall, 2003, O'Reilly
• Genomic Perl: From Bioinformatics Basics to Working Code, Rex A. Dwyer, 2002, Cambridge University Press
• Beginning Perl for Bioinformatics, James Tisdall, 2001, O'Reilly
• Developing Bioinformatics Computer Skills, Cynthia Gibas, Per Jambeck, 2001, O'Reilly

General Genomics

• Functional Microbial Genomics (Volume 33), Brendan Wren, Nick Dorrell, 2003, Academic Press
• Discovering Genomics, Proteomics, and Bioinformatics, A. Malcolm Campbell, Laurie J. Heyer, 2002, Benjamin/Cummings
• Genomes, Terence A. Brown, 2002, John Wiley & Sons
• Essentials of Medical Genomics, Stuart M. Brown , 2002, John Wiley & Sons
• A Primer of Genome Science, Greg Gibson, Spencer V. Muse, 2002, Sinauer Associates
• Pathogen Genomics: Impact on Human Health, Karen Joy, Phd Shaw (Editors), 2002, Humana Press
• Genomics, John E. Antonopoulos, 2000, Xlibris Corporation
• Genomics and Proteomics: Functional and Computational Aspects, Sandor Suhai (Editor), 2000, Plenum Pub Corp
• Functional Genomics: A Practical Approach (The Practical Approach Series, 235), S. Hunt and F. Livesey (Editors), 2000, Oxford Univ Press
• Human Molecular Genetics, Andrew P. Read, Tom Strachan 1999, BIOS Scientific Publishers Ltd.
• Genomics: The Science and Technology Behind the Human Genome Project, Charles R. Cantor and Cassandra L. Smith, 1999, John Wiley & Sons
• Cells: A Laboratory Manual, 3 volumes, David L. Spector, Robert D. Goldman, Leslie A. Leinwand, 1998, Cold Spring Harbor Laboratory Press
• Genome Analysis: A Laboratory Manual, 4 volumes, Bruce Birren, et al. (Editors), 1997, Cold Spring Harbor Laboratory Press
• The Human Genome Project, N. G. Cooper (Editor), 1994, University Science Books

Comparative Genomics

• Handbook of Comparative Genomics: Principles and Methodology, Cecilia Saccone, Graziano Pesole, 2003, Wiley-Liss
• Sequence - Evolution - Function: Computational Approaches in Comparative Genomics, Eugene V. Koonin, Michael Y. Galperin, 2002, Kluwer Academic Publishers
• Comparative Genomics - Empirical and Analytical Approaches to Gene Order Dynamics, Map Alignment and the Evolution of Gene Families, David Sankoff and Joseph H. Nadeau, 2000, Kluwer Academic Pub
• Comparative Genomics, Melody Clark (Editor), 2000, Kluwer Academic Pub

Proteomics

• Proteins and Proteomics: A Laboratory Manual, Richard J. Simpson (Editor), Cold Spring Harbor Laboratory
• Proteomics in Practice: A Laboratory Manual of Proteome Analysis , Reiner Westermeier, Tom Naven, 2002, John Wiley & Sons
• Posttranslational Modifications of Proteins: Tools for Functional Proteomics (Methods in Molecular Biology, Vol 194) , Christoph Kannicht (Editor), 2002, Humana Press
• Peptide Arrays on Membrane Supports: Synthesis and Applications (Springer Lab Manual), Joachim Koch, Michael Mahler (Editors), 2002, Springer Verlag
• Proteomics , Timothy Palzkill, 2002, Kluwer Academic Publishers
• Introduction to Proteomics: Tools for the New Biology , Daniel C. Liebler (Editor), 2001, Humana Press
• Proteome Research: Mass Spectrometry (Principles and Practice) , P. James (Editor), 2001, Springer Verlag
• Interpreting Protein Mass Spectra: A Comprehensive Resource , A. Peter Snyder, 2000, American Chemical Society
• Protein Sequencing and Identification Using Tandem Mass Spectrometry , Michael Kinter, Nicholas E. Sherman, 2000, Wiley-Interscience
• From Genome to Proteome: Advances in the Practice and Application of Proteomics, Michael J. Dunn (Editor), 2000, Vch Verlagsgesellschaft Mbh
• Proteomics: From Protein Sequence to Function, S. Pennington (Editor), M. Dunn (Editor), 2000, Springer Verlag
• Proteome Research: Two-Dimensional Gel Electrophoresis and Detection Methods (Principles and Practice), T. Rabilloud (Editor), 2000, Springer Verlag
• Proteome and Protein Analysis, R. M. Kamp, D. Kyriakidis, th Choli-Papadopoulou (Editor), 1999, Springer Verlag
• Proteome Research: New Frontiers in Functional Genomics, M. R. Wilkins, et al. (Editors), 1997, Springer Verlag

Protein Structure

• Structural Bioinformatics, Philip E. Bourne, Helge Weissig (Editors), 2003, John Wiley & Sons
• Protein Structure Prediction: Bioinfomatic Approach, I.F. Tsigelny, 2002, International University Line
• Introduction to Protein Architecture: The Structural Biology of Proteins, Arthur M. Lesk, 2001, Oxford University Press
• Protein Structure Prediction: Methods and Protocols, David M. Webster (Editor), 2000, Humana Press
• Introduction to Protein Structure, Carl-Ivar Branden, John Tooze, 1999, Garland Publishing
• Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, Alan Fersht, 1999, Freeman

Pharmacogenomics

• Pharmacogenomics: Social, Ethical, and Clinical Dimensions, Mark A. Rothstein (Editor), 2003, Wiley-Liss
• Pharmacogenomics: The Search for Individualized Therapies, Julio Licinio, Ma-Li Wong (Editors), 2002, John Wiley & Sons
• Pharmacogenomics, Werner Kalow, Urs A. Meyer, Rachel Tyndale (Editors), 2001, Marcel Dekker
• Pharmacogenetics and Pharmcogenomics: Recent Conceptual and Technical Advances (Pharmacology, Volume 61, Number 3, 2000), Elliot S. Vesell (Editor), 2000, S. Karger Publishing
• Pharmacogenetics, Wendell Weber, 1997, Oxford University Press

DNA Microarrays

• Statistical Analysis of Gene Expression Microarray Data, T. P. Speed (Editor), 2003, CRC Press
• Microarray Gene Expression Data Analysis: A Beginner's Guide, Helen C. Causton, John Quackenbush, Alvis Brazma, 2003, Blackwell Publishers
• The Analysis of Gene Expression Data (Statistics for Biology and Health), G. Parmigiani, E. S. Garrett, R. A. Irizarry, S. Zeger , Graeme Clark (Editors), 2003, Springer Verlag
• A Practical Approach to Microarray Data Analysis, Daniel P. Berrar, Werner Dubitzky, Martin Granzow (Editors), 2002, Kluwer Academic Publishers
• DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling, Pierre Baldi, G. Wesley Hatfield, 2002, Cambridge University Press
• DNA Microarrays: A Molecular Cloning Manual, David Bowtell, Joseph Sambrook (Editors), 2002, Cold Spring Harbor Laboratory
• DNA Array Image Analysis: Nuts & Bolts, Gerda Kamberova, Shishir Shah, 2002, DNA Press
• Microarray Analysis, Mark Schena, 2002, John Wiley & Sons
• A Biologist's Guide to Analysis of DNA Microarray Data, Steen Knudsen, 2002, John Wiley & Sons
• Microarrays for an Integrative Genomics (Computational Molecular Biology), Isaac S. Kohane, Alvin Kho, Atul J. Butte, 2002, MIT Press
• Microarrays for the Neurosciences: An Essential Guide (Cellular and Molecular Neuroscience), Daniel H. Geschwind, Jeffrey P. Gregg (Editors), 2002, MIT Press
• DNA Microarrays: Gene Expression Applications, Bertrand Jordan (Editor), 2001, Springer Verlag
• DNA Arrays: Methods and Protocols (Methods in Molecular Biology, Volume 170), Jang B. Rampal (Editor), 2001, Humana Press
• DNA Arrays: Technologies and Experimental Strategies, Elena V. Grigorenko (Editor), 2001, CRC Press
• Microarray Biochip Technology, Mark Schena (Editor), 2000, Eaton Pub
• Expression Genetics: Accelerated and High-Throughput Methods (Biotechniques Update Series), Michael McClelland (Editor), Arthur B. Pardee (Editor), 1999, Eaton Pub
• DNA Microarrays: A Practical Approach (Practical Approach Series 205), Mark Schena (Editor), 1999, Oxford Univ Press
• cDNA Preparation and Characterization (Methods in Enzymology Volume 303), S.M. Weissman (Editor), 1999, Academic Press

Systems Biology, Genetic and Biochemical Network

• Handbook of Graphs and Networks : From the Genome to the Internet, Stefan Bornholdt, Heinz Georg Schuster (Editors), 2003, Vch Verlagsgesellschaft Mbh
• Computational Cell Biology, Christopher Fall, Eric Marland, John Wagner, John Tyson (Editors), 2002, Springer Verlag
• Gene Regulation and Metabolism: Post-Genomic Computational Approaches (Computational Molecular Biology), Julio Collado-Vides, Ralf Hofestadt (Editors), 2002, MIT Press
• Foundations of Systems Biology, Hiroaki Kitano (Editor), 2001, MIT Press
• Genomic Regulatory Systems: Development and Evolution, Eric H. Davidson , 2001, Academic Press
• Genes & Signals, Mark Ptashne, Alexander Gann, 2001, Cold Spring Harbor Laboratory
• Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology), James M. Bower and Hamid Bolouri (Editors), 2001, MIT Press
• Protein-Protein Interactions: A Molecular Cloning Manual, Erica Golemis (Editor), 2001, Cold Spring Harbor Laboratory
• Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, Eberhard O. Voit, 2000, Cambridge University Press
• Mathematical Physiology, James P. Keener, James Sneyd, 1998, Springer Verlag

DNA Sequencing

• DNA Sequencing: From Experimental Methods to Bioinformatics (Introduction to Biotechniques Series), Luke Alphey, 1997, Springer Verlag
• Automated DNA sequencing and analysis, Adams M.D., Fields C., Venter J.C. (Editors), 1994, Academic Press

Apart from above mentioned books, you can also find some useful books links at following mentioned URLs:

http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

http://www.amazon.com/Bioinformatics-Genes-Proteins-Computers-Advanced/dp/1859960545

http://www.amazon.com/Introduction-Bioinformatics-Algorithms-Computational-Molecular/dp/0262101068

http://books.google.no/books?id=pxSM7R1sdeQC&dq=Pierre+baldi+%2B+bioinformatics&printsec=frontcover&source=bn&hl=en&ei=IoGRS6uCIJT-NYLA8Z0N&sa=X&oi=book_result&ct=result&redir_esc=y#v=onepage&q&f=false

http://www.amazon.com/Statistical-Methods-Bioinformatics-Introduction-Statistics/dp/0387400826

If you think your favourite books are not listed then please write it down in comment section for the benefits of other users. Feel free to add many more books in comment section.