First cover a full workflow from preparation, reads counting, data preprocessing, gene set test, to pathway visualization in about 40 lines of codes. The same workflow can be used for GO analysis or other types of gene set analysis too. We also describe joint workflows, i.e. to do gene-level analysis using one of the major RNA-Seq analysis tools, DEseq/DEseq2, edgeR, limma and Cufflinks, and feed the results into GAGE/Pahview for pathway analysis or visualization. All these workflows are implemented in R/Bioconductor.

The work ows cover the most common situations and issues for RNA-Seq data pathway analysis. Issues like data quality assessment are relevant for data analysis in general yet out the scope of this tutorial. Although we focus on RNA-Seq data here, but pathway analysis work ow remains similar for microarray, particularly step 3-4 would be the same. Please check gage and pathview vigenttes for details.

Note: You need to update to current release versions of R(3.0.2)/ Bioconductor(2.13) to use all the features. 

Reference: 

Please check it out:
http://bioconductor.org/packages/release/bioc/html/gage.html
http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf

Samtools

Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format

BCFtools

Reading/writing BCF2/VCF/gVCF files and calling/filtering/summarising SNP and short indel sequence variants

HTSlib

A C library for reading/writing high-throughput sequencing data

Samtools and BCFtools both use HTSlib internally, but these source packages contain their own copies of htslib so they can be built independently.

Address of the bookmark: http://www.htslib.org/

Dated : 22 Nov 2013 -23 Nov 2013

http://www.ngsasia-congress.com/

Workshops are conducted in regular intervals on Drug Designing, Protein Modeling and Simulation, Chemoinformatics, Bioinformatics etc.The workshops are highly beneficial for working professionals, students, researcher for enhancements of the skills in short duration.

More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us

- Plan more efficient experiments
- Correctly interpret results
- Communicate results in publications more effectively

The course focus is on methodologies, not on particular software tools. After the course participants should be able to apply the methods in their respective environment. However, during the course, hands-on sessions will be performed using the Genedata Expressionist® software, which enables participants to quickly apply the discussed methods and visualize results. No previous knowledge on Expressionist® is required; access to the software is free of charge during the course.

More @ http://www.dixa-fp7.eu/dixa-training/dixa-training-agenda/genedata-academy#!

Classificier of reads from metagenomic sequencing experiments.

•  Kawulok, J., Deorowicz, S., CoMeta: Classification of Metagenomes Using k-mers, PLOS ONE, 2015; 10(4):1–23,

CoMSA

Compressor of multiple sequence alignments of proteins.

•  Deorowicz, S., Walczyszyn, J., Debudaj-Grabysz, A., CoMSA: compression of protein multiple sequence alignment files, Bioinformatics, 2019; 35(2):22–234,

DSRC

Compressor of sequencing reads.

•  Roguski, L., Deorowicz, S., DSRC 2: Industry-oriented compression of FASTQ files, Bioinformatics, 2014; 30(15):2213–2215,
•  Deorowicz, S., Grabowski, Sz., Compression of DNA sequences in FASTQ format, Bioinformatics, 2011; 27(6):860–862,

FAMSA

Multiple sequence alignment designed for huge families of proteins (even containing hundreds of thousands of sequences).

•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., FAMSA: Fast and accurate multiple sequence alignment of huge protein families, Scientific Reports, 2016; 6(33964):

FaStore

Compressor of FASTQ files.

•  Roguski, L., Ochoa, I., Hernaez, M., Deorowicz, S., FaStore - a space-saving solution for raw sequencing data, Bioinformatics, 2018; 34(16):2748–2756,

FQSqueezer

Experimental high-end compressor of FASTQ files.

•  Deorowicz, S., FQSqueezer: k-mer-based compression of sequencing data, Scientific Reports, 2020; 10(578):

GDC

Compressor of collections of genome sequences.

•  Deorowicz, S., Danek, A., Niemiec, M., GDC 2: Compression of large collections of genomes, Scientific Reports, 2015; 5(11565):1–12,
•  Deorowicz, S., Grabowski, Sz., Robust relative compression of genomes with random access, Bioinformatics, 2011; 27(21):2979–2986,

GTC

Genotype databases compressor with support for fast queries.

•  Danek, A., Deorowicz, S., GTC: how to maintain huge genotype collections in a compressed form, Bioinformatics, 2018; 34(11):1834–1840,

GTShark

Genotypes compressor.

•  Deorowicz, S., Danek, A., GTShark: Genotype compression in large projects, Bioinformatics, 2019; 35(22):4791–4793,

KMC

Memory frugal k-mer counter.

•  Kokot, M., Długosz, M., Deorowicz, S., KMC 3: counting and manipulating k -mer statistics, Bioinformatics, 2017; 33(17):2759–2761,
•  Deorowicz, S., Kokot, M., Grabowski, Sz., Debudaj-Grabysz, A., KMC 2: Fast and resource-frugal k-mer counting, Bioinformatics, 2015; 31(10):1569–1576,
•  Deorowicz, S., Debudaj-Grabysz, A., Grabowski, Sz., Disk-based k-mer counting on a PC, BMC Bioinformatics, 2013; 14():Article no. 160,

Kmer-db

Tool for estimation of evolutionary distances in a collection of genomes.

•  Deorowicz, S., Gudys, A., Dlugosz, M., Kokot, M., Danek, A., Kmer-db: instant evolutionary distance estimation, Bioinformatics, 2019; 35(1):133–136,

MuGI

Index allowing queries for a collection of multiple genome sequences.

•  Danek, A., Deorowicz, S., Grabowski, Sz., Indexes of Large Genome Collections on a PC, PLOS ONE, 2014; 9(10):e109384,

ORCOM

Experimental compressor of sequencing reads.

•  Grabowski, Sz., Deorowicz, S., Roguski, L., Disk-based compression of data from genome sequencing, Bioinformatics, 2014; 31(9):1389–1395,

PgSA

Index allowing queries for a collection of sequencing reads.

•  Kowalski, T., Grabowski, Sz., Deorowicz, S., Indexing arbitrary-length k-mers in sequencing reads, PLOS ONE, 2015; 10(7):1–16,

QuickProbs

Multiple sequence alignment designed especially for GPU.

•  Gudys, A., Deorowicz, S., QuickProbs 2: towards rapid construction of high-quality alignments of large protein families, Scientific Reports, 2017; 7(41553):
•  Gudys, A., Deorowicz, S., QuickProbs – A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors, PLOS ONE, 2014; 9(2):e88901,

RECKONER

Read error corrector.

•  Maciej Długosz, M., Deorowicz, S., RECKONER: read error corrector based on KMC, Bioinformatics, 2017; 33(7):1086–1089,

TGC

Compressor of collections of genomes given in Variant Call Format (VCF) files.

•  Deorowicz, S., Danek, A., Grabowski, Sz., Genome compression: a novel approach for large collections, Bioinformatics, 2013; 29(20):2572–2578,

VCFShark

Compressor of VCF files.

•  Deorowicz, S., Danek, A., GTShark: Genotype compression in large projects, biorxiv.org, 2020; ():

Whisper

Experimental mapper of whole genome sequencing data.

•  Deorowicz, S., Gudys, A., Whisper 2: indel-sensitive short read mapping, bioRxiv.org, 2019; :
•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., Grabowski, Sz., Whisper: read sorting allows robust robust mapping of DNA sequencing data, Bioinformatics, 2019; 35(12):2043–2050,
•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., Grabowski, Sz., Robust mapping of whole genome sequencing data, Poster at The Biology of Genomes Conference, 2017;

Now my main interests are in gene regulation, where we work on promoter analysis; non-coding RNA, where miRNAs and structure prediction are the main areas; and protein structure, where the group is working on methods for structure prediction from sequence. To read more about these topics, please see the research pages.

Lab page @ http://wiki.binf.ku.dk/User:Krogh

Plants represent an untapped resource of natural bioactive compounds that significantly contribute to plant resilience to pathogens, herbivores, and abiotic stresses, and may be applied for medicine or crop protection. In this project, you will design and/or apply innovative omics integration strategies for genomics, transcriptomics, and metabolomics data, to discover plant specialized metabolite biosynthetic pathways and study their evolution. You will work together with the other PhD candidate in this project, which will entail a combination of algorithm development, greenhouse experiments, integrative omics analysis, and evolutionary genomics. Extensive local and international collaboration is foreseen, including possibilities for a foreign research visit as part of your PhD project.

The research is embedded within the chairs of Bioinformatics and Biosystematics. The projects will be (co-)supervised by Dr. Marnix Medema, Dr. Justin van der Hooft, Dr. Klaas Bouwmeester and Prof. Dr. Eric Schranz.

We ask

We are looking for two enthusiastic and complementary team players with all or a subset of the following skills:

a solid academic record (MSc) in bioinformatics, biology, or biotechnology
experience in computational omics analysis and proficiency in programming (in, e.g., Python)
at least basic to intermediate statistical and mathematical skills
demonstrable experience in working with next-generation sequencing data or with greenhouse experiments with plants
affinity with plant science, metabolism and/or biosynthetic pathways
you meet all the entry requirements of the WUR PhD programme.
More information

For more information about this position, please contact Marnix Medema, Associate Professor Bioinformatics, by email (marnix.medema@wur.nl).
For more information about the procedure, please contact vacaturemeldingen.psg@wur.nl.

Salary: Rs. 10,000/- per month.

Age limit: 35 for men and 40 for women & SC/ST.

Qualification: First class in M. Sc (Agriculture)/MCA/M.Sc (IT)/ M. Sc (Computer Application)/M.Sc (Bioinformatics)/M.Sc (Geoinformatics).

Desirable: Two years experience in web design and web programming.

Date & time of interview: 08.10.2013, 10 am

Interested candidates may appear for an interview at this institute along with their application in plain paper containing the following particulars viz. (1) Name (2) Father/Husband/Guardian’s Name (3) date of birth & age as on 01.10.2013 (4) Permanent address (5) Address for communication (6) Email address and Telephone No. with code (7) Qualification (8) National fellowship like ICAR/CSIR/UGC etc. if any (9) Whether SC/ST/OBC (10) Details of experience (Attested copies of degree certificate, proof of age, mark sheets). Original certificates should be produced for verification.

No TA/DA will be admissible to the candidates attending the test. The selected candidate will have to join immediately.

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