https://academic.oup.com/bioinformatics/article/30/18/2559/2475628
Reference-free SNP detection: dealing with the data deluge

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4083407/
GATB/DiscoSnp: DiscoSnp is designed for discovering all kinds of SNPs (not only isolated ones), as well as insertions and deletions, from raw set(s) of reads.

https://github.com/GATB/DiscoSnp
De novo assembly | Oxford Nanopore Technologies

https://nanoporetech.com/taxonomy/term/131
De novo long-read assembly of a complex animal genome | bioRxiv

https://www.biorxiv.org/content/early/2017/09/10/187054
Rapid de novo assembly of the European eel genome from nanopore sequencing reads | Scientific Reports

https://www.nature.com/articles/s41598-017-07650-6.epdf?author_access_token=dktG7e98wyRJnaEEMTcPqtRgN0jAjWel9jnR3ZoTv0P7E7t-wVGo30iojNO7dICajNY_7PE5xVPv6OoLe7hn9TeUjcZ5umREOzNoPMWkfYH58RS6uxm3vm4e4BG2AA_WKW84i6egKK271NwMq-NfzA%3D%3D
nanoporetech/ont-assembly-polish: ONT assembly and Illumina polishing pipeline

https://github.com/nanoporetech/ont-assembly-polish
Generade-nl/TULIP: TULIP - The Uncorrected Long read Itegration Pipeline

https://github.com/Generade-nl/TULIP
www.nature.com

https://www.nature.com/articles/s41598-017-03996-z
Example gallery of NanoPlot – Gigabase or gigabyte

https://gigabaseorgigabyte.wordpress.com/2017/06/01/example-gallery-of-nanoplot/
Tool documentation

https://broadinstitute.github.io/picard/command-line-overview.html
Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. - PubMed - NCBI

https://www.ncbi.nlm.nih.gov/pubmed/24185095
MAFFT ver.7 - a multiple sequence alignment program

https://mafft.cbrc.jp/alignment/software/algorithms/algorithms.html
Measuring the distance between multiple sequence alignments | Bioinformatics | Oxford Academic

https://academic.oup.com/bioinformatics/article/28/4/495/212883
The MUMmer 3 examples

http://mummer.sourceforge.net/examples/
MAFFT ver.7 - a multiple sequence alignment program

https://mafft.cbrc.jp/alignment/software/tips.html
Omega | Overlap-graph de novo Assembler for Metagenomics

https://omega.omicsbio.org/
abiswas-odu/Disco: Multi-threaded Distributed Memory Overlap-Layout-Consensus (OLC) Metagenome Assembler

https://github.com/abiswas-odu/Disco
SAGE: String-overlap Assembly of GEnomes | BMC Bioinformatics | Full Text

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-302

Fast and sensitive mapping of nanopore sequencing reads with GraphMap | Nature Communications

https://www.nature.com/articles/ncomms11307
lumpy-sv/extractSplitReads_BwaMem at master · arq5x/lumpy-sv

https://github.com/arq5x/lumpy-sv/blob/master/scripts/extractSplitReads_BwaMem
jts/nanocorrect: Experimental pipeline for correcting nanopore reads

https://github.com/jts/nanocorrect

video - how to install flash plugin on ubuntu 14.04 LTS 64-bit version - Ask Ubuntu

https://askubuntu.com/questions/469553/how-to-install-flash-plugin-on-ubuntu-14-04-lts-64-bit-version
lh3/fermi: A WGS de novo assembler based on the FMD-index for large genomes

https://github.com/lh3/fermi
Multi-metagenome

http://madsalbertsen.github.io/multi-metagenome/docs/step9.html
Bandage by rrwick

https://rrwick.github.io/Bandage/
Codon Optimization OnLine (COOL): a web-based multi-objective optimization platform for synthetic gene design | Bioinformatics | Oxford Academic

https://academic.oup.com/bioinformatics/article/30/15/2210/2391162
Genome Architecture and Evolution of a Unichromosomal Asexual Nematode - ScienceDirect

https://www.sciencedirect.com/science/article/pii/S096098221731076X?via%3Dihub#fig4
How to determine chimeras in my de novo assembly? - SEQanswers

http://seqanswers.com/forums/showthread.php?t=26721
samtools(1) manual page

http://www.htslib.org/doc/samtools.html
How To Filter Mapped Reads With Samtools

https://www.biostars.org/p/56246/
The MUMmer 3 manual

http://mummer.sourceforge.net/manual/#nucmer
assembly_olc.pdf

http://www.cs.jhu.edu/~langmea/resources/lecture_notes/assembly_olc.pdf
SAM and BAM filtering oneliners

https://gist.github.com/davfre/8596159
Inroduction to dot-plots

http://www.code10.info/index.php%3Foption%3Dcom_content%26view%3Darticle%26id%3D64:inroduction-to-dot-plots%26catid%3D52:cat_coding_algorithms_dot-plots%26Itemid%3D76
RepeatFinder Home Page

http://www.cbcb.umd.edu/software/RepeatFinder/
RepeatFinderReprint.pdf

http://www.cbcb.umd.edu/software/RepeatFinder/RepeatFinderReprint.pdf
https://bernatgel.github.io/karyoploter_tutorial//Tutorial/CreateIdeogram/CreateIdeogram.html

https://bernatgel.github.io/karyoploter_tutorial//Tutorial/CreateIdeogram/CreateIdeogram.html
Circular Visualization in R

http://zuguang.de/circlize_book/book/introduction.html#a-qiuck-glance
Creating a coverage plot using BEDTools and R

https://davetang.org/muse/2015/08/05/creating-a-coverage-plot-using-bedtools-and-r/
Eval: A software package for analysis of genome annotations | BMC Bioinformatics | Full Text

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-4-50
eval-documentation.pdf

http://mblab.wustl.edu/media/software/eval-documentation.pdf
OmicCircos: A Simple-to-Use R Package for the Circular Visualization of Multidimensional Omics Data

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3921174/
sequence - download.tardigrades.org > v1 > sequence

http://download.tardigrades.org/v1/sequence/
ksahlin/BESST: BESST - scaffolder for genomic assemblies

https://github.com/ksahlin/BESST
reubwn/scripts: Useful scripts for various things

https://github.com/reubwn/scripts
ICEberg

http://db-mml.sjtu.edu.cn/ICEberg/index.php
Satsuma - Evolution and Genomics

http://evomics.org/learning/genomics/satsuma/
A complete bacterial genome assembled de novo using only nanopore sequencing data | Nature Methods

https://www.nature.com/articles/nmeth.3444
vezzi/FRC_align: Computes FRC from SAM/BAM file and not from afg files

https://mail.google.com/mail/u/0/#inbox
Read GTF file into R - Dave Tang's blog

https://davetang.org/muse/2017/08/04/read-gtf-file-r/

https://bernatgel.github.io/karyoploter_tutorial//Tutorial/CustomGenomes/CustomGenomes.html

https://bernatgel.github.io/karyoploter_tutorial//Tutorial/CustomGenomes/CustomGenomes.html
Dot: Interactive dot plot for genome-genome alignments

https://dnanexus.github.io/dot/
Zoho Accounts

https://accounts.zoho.eu/signin?servicename=ZohoProjects&serviceurl=https%3A%2F%2Fprojects.zoho.eu%2Fportal%2Favaga2
lh3/minimap2: A versatile pairwise aligner for genomic and spliced nucleotide sequences

https://github.com/lh3/minimap2
SSPACE-LongRead: scaffolding bacterial draft genomes using long read sequence information | BMC Bioinformatics | Full Text

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-211
Palindromic gene amplification — an evolutionarily conserved role for DNA inverted repeats in the genome | Nature Reviews Cancer

https://www.nature.com/articles/nrc2591
bioinformatics - BLAST DNA Sequences Reversed - Biology Stack Exchange

https://biology.stackexchange.com/questions/8160/blast-dna-sequences-reversed
LASTZ

http://www.bx.psu.edu/miller_lab/dist/README.lastz-1.02.00/README.lastz-1.02.00a.html
SOGo - (1652) Inbox

https://sogo.unamur.be/SOGo/so/jnarayan/Mail/view
Tetra-Nucleotide Analysis (TNA) | BIOiPLUG Help center

http://help.bioiplug.com/tetra-nucleotide-analysis-tna/

Clustering metagenomic contigs on tetranucleotide frequency — CGAT documentation

http://cgat.readthedocs.io/en/latest/recipes/metagenome_contigs_kmers.html

Next generation quantitative genetics in plants. Jiménez-Gómez, Frontiers in Plant Science 2:77, 2011 Full Text [equally relevant to animal and microbial systems]

Sense from sequence reads: methods for alignment and assembly. Flicek & Birney, Nat Methods 6(11 Suppl):S6-S12, 2009. Full Text

Library construction and experimental design

Statistical design and analysis of RNA sequencing data. Auer & Doerge, Genetics 185(2):405-16, 2010. PubMedCentral

Biases in Illumina transcriptome sequencing caused by random hexamer priming. Hansen et al., Nucleic Acids Res. 38(12): e131, 2010. PubMedCentral

Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Aird et al, Genome Biology 12:R18, 2011 Full Text

Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of GC-biased genomes. Kozarewa et al, Nature Methods 6(4):291-5, 2009 PubMedCentral

Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Rohland & Reich, Genome Research 22(5): 939–946. PubMedCentral

Data formats, data management, and alignment software tools

The Sequence Alignment/Map format and SAMtools. Li et al, Bioinformatics 25(16):2078-9, 2009 PubMedCentral

SAM format specification file

Efficient storage of high throughput sequencing data using reference-based compression. Fritz et al, Genome Res 21(5):734-40, 2011. Full Text

Compression of DNA sequence reads in FASTQ format. Deorowicz & Grabowski, Bioinformatics 27(6):860-2, 2011. PubMed

Fast and accurate short read alignment with Burrows-Wheeler transform. Li & Durbin, Bioinformatics 25(14):1754-60, 2009. PubMedCentral

Improving SNP discovery by base alignment quality. Li H, Bioinformatics 27(8):1157-8, 2011. PubMed

BEDTools: a flexible suite of utilities for comparing genomic features. Quinlan and Hall, Bioinformatics 26:841-842, 2010. Publisher Website

Data quality assessment, filtering, and correction

SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. Cox et al, BMC Bioinformatics 11:485, 2010. PubMedCentral

TileQC: a system for tile-based quality control of Solexa data. Dolan & Denver, BMC Bioinformatics 9:250, 2008 PubMedCentral [requires a reference sequence]

Quake: quality-aware detection and correction of sequencing errors. Kelley et al, Genome Biol 11(11):R116, 2010. PubMed

FastQC: a quality control tool for high-throughput sequence data. Home Page

FASTX-toolkit: FASTQ/A short-reads pre-processing tools Home Page

Reference-free validation of short read data. Schröder et al, PLoS One 5(9):e12681, 2010. PubMedCentral

Correction of sequencing errors in a mixed set of reads. Salmela, Bioinformatics 26(10):1284, 2010. Full Text [includes error correction of SOLiD reads in colorspace]

Repeat-aware modeling and correction of short read errors. Yang et al, BMC Bioinformatics 12(Supp1):S52, 2011 PubMedCentral [requires a reference sequence]

HiTEC: accurate error correction in high-throughput sequencing data. Ilie et al, Bioinformatics 27(3):295, 2011 Full Text

Error correction of high-throughput sequencing datasets with non-uniform coverage. Medvedev et al., Bioinformatics 27(13):i137-41, 2011. PubMedCentral

De novo assembly

Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Zerbino & Birney, Genome Res 18(5):821-9, 2008. u>PubMedCentral

Assembly of large genomes using second-generation sequencing. Schatz et al, Genome Res 20(9):1165-73, 2010. PubMedCentral

High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Gnerre et al, PNAS 108(4): 1513-18, 2011 PubMedCentral

Genome assembly has a major impact on gene content: a comparison of annotation in two Bos taurus assemblies. Florea  et al., PLoS One 6(6):e21400, 2011. PubMedCentral

Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Carver et al, Bioinformatics 28(4):464 - 469, 2012 PubMedCentral

Efficient de novo assembly of large genomes using compressed data structures. Simpson & Durbin, Genome Research 22:549-556, 2012 Full Text [Describes the String Graph Assembler (SGA), which assembled a human genome in less than 6 days using 54 Gb of RAM and a 123-processor compute cluster for calculation of an FM-index of the 1.2 billion reads]

Readjoiner: a fast and memory efficient string graph-based sequence assembler. Gonnella & Kurtz, BMC Bioinformatics 13: 82, 2012 PubMedCentral

Assemblathon 1: A competitive assessment of de novo short read assembly methods. Earl et al, Genome Research 21:2224-2241, 2011 Full Text

Chromatin immunoprecipation analysis: ChIP-seq

ChIP-seq: advantages and challenges of a maturing technology. Park, Nat Rev Genet. 10:669-80, 2009 PubMed

ChIP-seq and Beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Furey, Nat Rev Genet 13: 840–852, 2012 Publisher Web Site

MuMoD: a Bayesian approach to detect multiple modes of protein–DNA binding from genome-wide ChIP data. Narlikar, Nucleic Acids Res 41:21–32, 2013 PubMed

Transcriptome analysis

Assembly and comparison to genome

Full-length transcriptome assembly from RNA-Seq data without a reference genome. Grabherr et al, Nature Biotechnology 29:644 - 652, 2011. PubMed [The software is called Trinity, and is available on Sourceforge.]

Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Peng et al, Nature Biotechnology 30:253 - 260, 2012. PubMed [Several comments on this paper question whether the reported differences are in fact evidence of editing or are simply sequencing errors - the authors stand by their conclusions, but the controversy demonstrates the importance of robust data analysis methods.]

Optimization of de novo transcriptome assembly from next-generation sequencing data. Surget-Groba & Montoya-Burgos, Genome Res 20(10):1432-40, 2010. Full Text

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads. Martin et al, BMC Genomics 11:663, 2010 Full Text

De novo assembly and analysis of RNA-seq data. Robertson et al, Nature Methods 7:909-912, 2010 Full Text [describes Trans-ABySS, a pipeline to use the ABySS parallel assembler for de novo transcriptome analysis]

Differential expression analysis

R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data. Mittal & McDonald, Nucleic Acids Res, 2012 Full Text

Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Mercer et al, Nature Biotechnology 30:99 - 104, 2012 Publisher Website

Differential gene and transcript expression analysis of RNA-Seq experiments with TopHat and Cufflinks. Trapnell et al, Nature Protocols 7:562 - 578, 2012 Publisher Website

Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Łabaj et al, Bioinformatics 27:i383 - i391, 2011 Full Text

Improving RNA-Seq expression estimates by correcting for fragment bias. Roberts et al, Genome Biol 12:R22, 2011 PubMed Central

Cloud-scale RNA-sequencing differential expression analysis with Myrna. Langmead et al, Genome Biol 11:R83, 2010 Full Text

From RNA-seq reads to differential expression results. Oshlack et al, Genome Biol 11(12):220, 2010 Full Text

DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Wang et al., Bioinformatics. 26(1):136-8. 2010 PubMed

DEseq: Differential expression analysis for sequence count data. Anders and Huber, Genome Biology 11:R106, 2010 Full Text

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Robinson et al., Bioinformatics 26(1):139-40 2010 PubMedCentral

Two-stage Poisson model for testing RNA-seq data. Auer and Doerge, SAGMB 10(1), article 26 Full Text

Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. McCormick et al., Silence2(1):2, 2011 PubMedCentral

RNA-Seq gene expression estimation with read mapping uncertainty. Li et al, Bioinformatics 26:493-500, 2010 PubMedCentral [describes the RSEM software package]

Comparing genomes and assemblies; variant detection

Versatile and open software for comparing large genomes. Kurtz et al, Genome Biol (5(2):R12, 2004. PubMedCentral [describes the MUMmer software for full-genome alignment & comparisons]

Searching for SNPs with cloud computing. Langmead et al, Genome Biol 10(11):R134, 2009 Full Text

Calling SNPs without a reference sequence. Ratan et al, BMC Bioinformatics 11:130, 2010 PubMedCentral

Microindel detection in short-read sequence data. Krawitz et al, Bioinformatics 26(6):722-9, 2010. Full Text

vipR: variant identification in pooled DNA using R. Altmann et al., Bioinformatics 27: i77-i84, 2011. PubMedCentral

Geoseq: a tool for dissecting deep-sequencing datasets. Gurtowski et al, BMC Bioinformatics 11:506, 2010. PubMedCentral [Geoseq is a web service that allows searching deep sequencing datasets with a reference sequence of a gene of interest]

Detecting and annotating genetic variations using the HugeSeq pipeline. Lam et al, Nature Biotechnology 30:226 - 229, 2012 Publisher Website, Home Page

Genome-wide LORE1 retrotransposon mutagenesis and high-throughput insertion detection in Lotus japonicus. Urbański et al, Plant J 64:731-741, 2012. Publisher Website [This paper describes a 2-dimensional pooling strategy with barcoding to allow use of Illumina sequencing to screen for retrotransposon insertion mutations, and includes a software package called FSTpoolit for analysis of the resulting sequence reads.]

Genotyping by sequencing

Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Davey et al., Nat Rev Genet 12(7):499-510, 2011 PubMed [A review of methods available at the time]

A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. Elshire et al., PLoS One 6(5):e19379, 2011. Full Text

Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. Poland et al., PLoS One 7(2): e32253, 2012. Full Text

Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. Peterson et al, PLoS One 7(5):e37135, . 2012. Full Text

Imputation of unordered markers and the impact on genomic selection accuracy. Rutkowski et al, G3 3(3):427-39, 2013. Full Text

Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high-throughput, highly informative genotyping for molecular breeding of Eucalyptus. Sansaloni et al., BMC Proceedings 5(Suppl 7):P54, 2011 Full Text

High-throughput genotyping by whole-genome resequencing. Huang et al., Genome Res 19(6):1068-76, 2009. Full Text

Multiplexed shotgun genotyping for rapid and efficient genetic mapping. Andolfatto et al. Genome Res 21(4):610-7, 2011. Full Text

Restriction-site Associated DNA (RAD) markers

Rapid SNP discovery and genetic mapping using sequenced RAD markers. Baird et al, PLoS One 3(10):e3376, 2008 Full Text

Linkage mapping and comparative genomics using next-generation RAD sequencing of a non-model organism. Baxter et al., PLoS One 6(4):e19315, 2011. Full Text

Genome evolution and meiotic maps by massively parallel DNA sequencing: spotted gar, an outgroup for the teleost genome duplication. Amores et al, Genetics 188(4):799-808, 2011. PubMed

Construction and application for QTL analysis of a Restriction-site Associated DNA (RAD) linkage map in barley. Chutimanitsakun et al, BMC Genomics 4; 12:4, 2011. Full Text

RAD tag sequencing as a source of SNP markers in Cynara cardunculus L. Scaglione et al., BMC Genomics 13:3, 2012. Full Text

Paired-end RAD-seq for de novo assembly and marker design without available reference. Willing et al., Bioinformatics 27(16):2187-93, 2011. Publisher Website

Local de novo assembly of RAD paired-end contigs using short sequencing reads. Etter et al., PLOS ONE 6(4): e18561, 2011. Full Text

Stacks: building and genotyping loci de novo from short-read sequences. Catchen et al., G3: Genes, Genomes, Genetics, 1:171-182, 2011. Full Text, Home Page

Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads. Chong et al, Bioinformatics 28(21):2732-7, 2012. Publisher Website

UK RAD Sequencing Wiki page, with bibliography and RADTools software download Home Page

Workspace environments

Papers

Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Goecks et al, Genome Biol 11(8):R86, 2010 PubMedCentral

Galaxy Cloudman: Delivering compute clusters. BMC Bioinformatics 11(Suppl. 12):S4, 2010 Full Text

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. McKenna et al, Genome Res 20(9):1297-303, 2010. PubMedCentral

A framework for variation discovery and genotyping using next-generation DNA sequencing data. DePristo et al., Nat Genet 43(5):491-8, 2011. PubMed

Online resources

The R statistical computing environment includes Bioconductor, a specialized set of tools for analysis of microarray and high-throughput sequencing data. Introductory materials from on-line or short workshops are widely available online; examples are Evomics2012 Bioconductor-tutorial.pdf, and Intro to Bioconductor. Materials from an advanced course on high-throughput genetic data analysis are at Seattle 2012 materials. Thomas Girke of UC-Riverside has written a very complete set of manuals describing the use of R and Bioconductor for analysis of genomic datasets, available at R and Bioconductor Manuals.
Manuals and contributed documentation for R are available at the R-project.org website, and video tutorials are also available on Youtube; those posted by Tutorlol are brief, clear, and to the point.
Materials from a series of mini-courses in R taught in 2010 at UCLA are available:

• Intro to programming and graphics
• Data manipulation and functions
• Graphics for exploratory data analysis
• Introductory statistics
• Linear regression

A Little Book of R for Bioinformatics is an on-line resource with information and exercises to provide practice in bioinformatics analysis of DNA sequences and other biological data in R.
Many books on specific topics in R programming are also available through Amazon or other vendors.

Cloud computing resources

The case for cloud computing in genome informatics. Lincoln Stein, Genome Biol. 11(5):207, 2010 Pubmed

Galaxy Cloudman: delivering cloud compute clusters. Afgan et al, BMC Bioinformatics 11(Suppl 12):S4, 2010 Full Text

CloudBioLinux is an open-source project that provides a bioinformatics Linux system for cloud computing, pre-configured with a variety of software tools installed and ready to use.

A tutorial on getting started with CloudBioLinux on the Amazon Web Services Elastic Compute Cloud (EC2)

“Deploying Galaxy on the Cloud” – slides from a presentation by Enis Afgan (Emory University) at the
 Bioinformatics Open Source Conference in Boston, July 2010

A screencast that provides a step-by-step guide to starting a Galaxy cluster in the EC2 environment

A webpage that has the same information in text form, and is the basis for the screencast

The iPlant Collaborative, an NSF-funded project to create computational resources for plant biology research, provides access to cloud computing resources through Atmosphere

SeqWare Query Engine: storing and searching sequence data in the cloud. O’Connor et al, BMC Bioinformatics 11(Suppl 12):S2, 2010 Full Text

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. Taylor, BMC Bioinformatics 11(Suppl 12):S1, 2010 Full Text

Links to Linux command-line tutorials and resources

Tutorials for AWK, a powerful tool for handling data tables

• A set of awk notes from Boston University
• Bruce Barnett's awk tutorial
• Greg Goebel's awk tutorial
• Executing an awk command from R to simplify data exploratory analysis, from Lex Nederbragt

Tutorials for bash shell scripting

• A tutorial at linuxconfig.org
• A Getting Started With Bash tutorial at hypexr.org
• Mendel Cooper's Advanced Bash Shell-Scripting Guide

Tutorials for sed, the command-line stream editor

• A tutorial at Rutgers
• Peteris Krumins claims to have the World's Best Introduction to Sed; take a look and judge for yourself.
• Bruce Barnett's sed tutorial.

Links to other useful sites

The SEQanswers online community has forums on several topics related to sequencing; the bioinformatics forum is the most active.

The SEQanswers Software Wiki is a list of software for analysis of sequencing data

Biostar is another online community for questions and answers on bioinformatics and computational genomics.

Information on file formats used by the University of California - Santa Cruz Genome Browser is on the FAQ list

A manual for the Integrated Genome Browser visualization tool is here

Course materials for a short course entitled Introduction to R and Bioconductor, held in Seattle in Dec 2010

Genomic Regions Enrichment of Annotations Tool - A web service to test for over-representation of specific ontology categories among genes near ChIP-seq peaks

Next-gen-seq software - a list of software packages, both commercial and open-source, related to analysis of deep sequencing datasets

Software from the Center for Bioinformatics and Computational Biology, University of Maryland - many useful programs, all open-source

PLAZA: a comparative genomics resource to study gene and genome evolution in plants; described by Proost et al, Plant Cell 21:3718, 2010 Full Text

The European Bioinformatics Institute provides tools ArrayExpressHTS and R-Cloud for analysis of transcriptome data

1. 2 Eggs and 100 Floor Classic Puzzle
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Specially for Microsoft Interview Puzzles, you may refer,
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Top 25 Tech Interview Logical Puzzles

Each of the puzzles got repeated a number of times in interviews even for top tech companies 

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Awk is a programming language that is specifically designed for quickly manipulating space delimited data. Although you can achieve all its functionality with Perl, awk is simpler in many practical cases.

Why awk? You can replace a pipeline of 'stuff | grep | sed | cut...' with a single call to awk. For a simple script, most of the timelag is in loading these apps into memory, and it's much faster to do it all with one. This is ideal for something like an openbox pipe menu where you want to generate something on the fly. You can use awk to make a neat one-liner for some quick job in the terminal, or build an awk section into a shell script. You can find a lot of online tutorials, but here I will only show a few examples which cover most of bioinformatician daily uses of awk.

choose rows where column 3 is larger than column 5:

awk '$3>$5' input.txt > output.txt

extract column 2,4,5:

awk '{print $2,$4,$5}' input.txt > output.txt

awk 'BEGIN{OFS="\t"}{print $2,$4,$5}' input.txt

show rows between 20th and 80th:

awk 'NR>=20&&NR<=80' input.txt > output.txt

calculate the average of column 2:

awk '{x+=$2}END{print x/NR}' input.txt

regex (egrep):

awk '/^test[0-9]+/' input.txt

calculate the sum of column 2 and 3 and put it at the end of a row or replace the first column:

awk '{print $0,$2+$3}' input.txt

awk '{$1=$2+$3;print}' input.txt

join two files on column 1:

awk 'BEGIN{while((getline<"file1.txt")>0)l[$1]=$0}$1 in l{print $0"\t"l[$1]}' file2.txt > output.txt

count number of occurrence of column 2 (uniq -c):

awk '{l[$2]++}END{for (x in l) print x,l[x]}' input.txt

apply "uniq" on column 2, only printing the first occurrence (uniq):

awk '!($2 in l){print;l[$2]=1}' input.txt

count different words (wc):

awk '{for(i=1;i!=NF;++i)c[$i]++}END{for (x in c) print x,c[x]}' input.txt

deal with simple CSV:

awk -F, '{print $1,$2}'

substitution (sed is simpler in this case):

awk '{sub(/test/, "no", $0);print}' input.txt

OK now here's where to read this stuff properly explained. roll

Two thorough tutorials:

http://www.gnu.org/software/gawk/manual/gawk.html

http://www.grymoire.com/Unix/Awk.html

A famous list of useful one-liners - though they're short, many are quite tricky:

http://www.pement.org/awk/awk1line.txt

And some nice explanations of those one-liners. After reading this you'll have a pretty good grasp!

http://www.catonmat.net/blog/awk-one-li … -part-one/

http://www.catonmat.net/blog/ten-awk-ti … -pitfalls/

a) Show appreciation for their time and thank them

b) Mention something specific you talked about in the interview, so they know it’s not a cut & paste email

c) Express interest in the position and tell them you’re excited to learn more

d) Invite them to contact you if they have any questions/concerns, or need clarification on anything discussed

First sample:

Dear Dr XYZ
I enjoyed speaking with you today about the XXX position at the X Lab, Uni. The job seems to be an excellent match for my skills and interests.

The lab loaded with new updated technology and international experts, that you informed while interviewing confirmed my desire to work with X lab.

In addition to my enthusiasm, I will bring to the position strong writing skills, assertiveness, and the ability to encourage others to work cooperatively with the group

I appreciate the time you took to interview me. I am very interested in working with you and look forward to hearing from you regarding this position.

Sincerely,
XXX

Second sample:

Dear Dr XXX,

I wanted to take a second to thank you for your time . I enjoyed our conversation about and enjoyed learning about the position overall.

It sounds like an exciting opportunity, and an opportunity I could succeed and excel in! I’m looking forward to hearing any updates you can share, and don’t hesitate to contact me if you have any questions or concerns in the meantime.

Thanks again for the great conversation .

Best Regards,
XXX

(Indian Council of Medical Research )
Jehangir Merwanji Street, Parel, Mumbai 400 012

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