bcftools: a set of companion tools, currently bundled with SAMtools, for identifying and filtering genomics variants.

bowtie: widely used, open source alignment software for aligning raw sequence reads to a reference genome.

BAM Format: binary, compressed format for storing SAM data.

BCF Format: Binary call format. Binary, compressed format for storing VCF data.

CIGAR String: Compact Idiosyncratic Gapped Alignment Report. A compact string that (partially) summarizes the alignment of a raw sequence read to the reference genome. Three core abbreviations are used: M for alignment match; I for insertion; and D for Deletion. For example, a CIGAR string of 5M2I63M indicates that the first 5 base pairs of the read align to the reference, followed by 2 base pairs, which are unique to the read, and not in the reference genome, followed by an additional 63 base pairs of alignment.

FASTA Format: text format for storing raw sequence data. For example, the FASTA file at: http://www.ncbi.nlm.nih.gov/nuccore/NC_008253 contains entire genome for Escherichia coli 536.

FASTQ Format: text format for storing raw sequence data along with quality scores for each base; usually generated by sequencing machines.

genotype likelihood: the probability that a specific genotype is present in the sample of interest. Genotype likelihoods are usually expressed as a Phred-scaled probability, where P = 10 ^ (-Q/10). For example, if the genotype TT (both alleles are T) at position 1,299,132 in human chromosome 12 (reference G) is 37, this translates to a probability of 10^-37/10 = 0.0001995, meaning that there is very low probability that the reads in your sample support a TT genotype. On the other hand, a genotype of AA at the same position with a score of 0 translates into a probability of 10^-0 = 1, indicating extremely high probability that your sample contains a homozygous mutation of G to A.

mate-pair: in paired-end sequencing, both ends of a single DNA or RNA fragment are sequenced, but the intermediate region is not. The two ends which are sequenced form a pair, and are frequently referred to as mate-pairs.

QNAME: unique identifier of a raw sequence read (also known as the Query Name). Used in FASTQ and SAM files.

paired-end sequencing: sequencing process where both ends of a single DNA or RNA fragment are sequenced, but the intermediate region is not. Particularly useful for identifying structural rearrangements, including gene fusions.

Phred-scaled probability: a scaled value (Q) used to compactly summarize a probability, where P = 10^-Q/10. For example, a Phred Q score of 10 translates to probability (P) = 10^-10/10 = 0.1. Phred-scaled probabilities are common in next-generation sequencing, and are used to represent multiple types of quality metrics, including quality of base calls, quality of mappings, and probabilities associated with specific genotypes. The name Phred refers to the original Phred base-calling software, which first used and developed the scale.

Phred quality score: a score assigned to each base within a sequence, quantifying the probability that the base was called incorrectly. Scores use a Phred-scaled probability metric. For example, a Phred Q score of 10 translates to P=10^-10/10 = 0.1, indicating that the base has a 0.1 probability of being incorrect. Higher Phred score correspond to higher accuracy. In the FASTQ format, Phred scores are represented as single ASCII letters. For details on translating between Phred scores and ASCII values, refer to Table 1 of this useful blog post from Damian Gregory Allis.

read-length: the number of base pairs that are sequenced in an individual sequence read.

read-depth: the number of sequence reads that pile up at the same genomic location. For example, 30X read-depth coverage indicates that the genomic location is covered by 30 independent sequencing reads. Increased read-depth translates into higher confidence for calling genomic variants.

RNAME: reference genome identifier (also known as the Reference Name). Within a SAM formatted file, the RNAME identifies the reference genome where the raw read aligns.

SAM Flag: a single integer value (e.g. 16), which encodes multiple elements of meta-data regarding a read and its alignment. Elements include: whether the read is one part of a paired-end read, whether the read aligns to the genome, and whether the read aligns to the forward or reverse strand of the genome. A useful online utility decodes a single SAM flag value into plain English.

SAM Format: Text file format for storing sequence alignments against a reference genome. See also BAM Format.

SAMtools: widely used, open source command line tool for manipulating SAM/BAM files. Includes options for converting, sorting, indexing and viewing SAM/BAM files. The SAMtools distribution also includes bcftools, a set of command line tools for identifying and filtering genomics variants. Created by Heng Li, currently of the Broad Institute.

single-read sequencing: sequencing process where only one end of a DNA or RNA fragment is sequenced. Contrast with paired-end sequencing.

VCF Format: Variant call format. Text file format for storing genomic variants, including single nucleotide polymorphisms, insertions, deletions and structural rearrangements. See also BCF format.

NextGenerationSequencing
A high-throughput sequencing method which parallelizes the sequencing process, producing thousands or millions of sequences at once.

DeepSequencing
Techniques of nucleotide sequence analysis that increase the range, complexity, sensitivity, and accuracy of results by greatly increasing the scale of operations and thus the number of nucleotides, and the number of copies of each nucleotide sequenced.

Paired-EndSequencing
Sequence both ends of the same fragment and keep track of the paired data.

Adapter
Short oligonucleotides which are attached to the DNA to be sequenced. An adapter can provide a priming site for both amplification and sequencing of the adjoining, unknown nucleic acid.

Library
A collection of DNA fragments with adapters ligated to each end.

BridgeAmplification
Generation of in situ copies of a specific DNA molecule on an oligo-decorated solid support.

EmulsionPCR
A method for bead-based amplification of a library. A single adapter-bound fragment is attached to the surface of a bead, and an oil emulsion containing necessary amplification reagents is formed around the bead/fragment component. Parallel amplification of millions of beads with millions of single strand fragments produces a sequencer-ready library.

Alignment
Mapping of sequence reads to a known reference sequence

Referencesequence/genome 
A fully assembled version of a genome that can be used for mapping short DNA sequence reads for comparisons of genomes from various individuals

CoverageDepth
The number of nucleotides from reads that are mapped to a given position of reference genome.

Specificity 
The percentage of sequences that map to the intended targets out of total bases per run.

Uniformity 
The variability in sequence coverage across target regions.

Homopolymer
Uninterrupted stretch of a single nucleotide type (e.g., TTT or GGGGGG)

InDel
InDel stands for Insertion or deletion. A form of structural variation in which a DNA segment is either deleted or inserted.

SNP 

SNP stands for Single Nucleotide Polymorphism. A single base difference found when comparing the same DNA sequence from two different individuals.

Abstract: Strand NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes Transcriptome / Genome alignment, Differential expression analysis with Statistical approach and Splicing events detection. Strand NGS also supports novel discovery like identification of novel genes, exons and Novel splice junctions, alongside it can also detect gene fusion events. Further downstream analysis such as GO and pathway analysis can be performed on the set of interesting genes. The product has an option to create pipelines for time consuming jobs which automates analysis and leaves more time for end data interpretation. This webinar will give an overview of the features in the RNA-Seq data analysis workflow in Strand NGS and also highlights on parameters within each feature that can be optimized depending on datasets and analysis needs.

Speaker: Mr. Sugandan Sivamani, Senior Application Scientist, Strand Life Sciences

Date: 9th Nov, Session 1 for SAPK/ APFO: 2:30 PM IST Date: 9th Nov, Session 2 for AFO/ EMEA: 9:00 AM PST

Register here http://www.strand-ngs.com/webinar_registration

http://compbio.cs.toronto.edu/prism/

Address of the bookmark: http://compbio.cs.toronto.edu/prism/

Address of the bookmark: https://github.com/marbl/Krona/wiki

The work in our lab has focussed on computational approaches to speed-up the finishing process. Specifically, we have explored the use of optical mapping and mate-pair data to augment assemblies and direct finishing experiments. The tools developed in our lab have been used in several finishing projects, producing complete genomes (and near-complete ones) with surprisingly little computational and experimental effort (Nagarajan et al., in submission). The executables (as well as source code) for these tools are freely available here:

• Scaffolding using Optical Restriction Mapping
  Optical Maps are global, ordered maps of restriction site locations in a genome. This information can be quite useful in scaffolding contigs from a shotgun assembly to guide the finishing process. A set of programs to exploit optical maps for assembly can be found here: SOMA v2.0 (63 MB tar.gz file). This version of SOMA contains several improvements to programs in v1.0 as well as new scripts for working with multiple maps, contig graphs and scaffolds. 
  
  
• Augmenting assemblies with mate-pair data
  Mate-pair information can be valuable in augmenting short-read assemblies and reconstructing the genome as larger scaffolds. AMOS-Hybrid is a pipeline written in the AMOS framework (open-source assembly tools) to merge arbitrary mated reads into an existing assembly and merge contigs and create scaffolds where possible. Source code and executables for AMOS-Hybrid are available here: AMOS-Hybrid v1.0 (142 MB tar.gz file). 
  
  
• Assembly and sequence-composition guided finishing
  Contigs from a shotgun assembly are typically linked together in a graph structure that can serve to guide finishing and in some case close gaps in-silico. Also, in many cases, sequence composition of contigs can provide clues to fill gaps in scaffolds. A set of scripts to automate some of these tasks can be found here: Finishing Scripts v1.0 (63 MB tar.gz file). 

http://www.cbcb.umd.edu/finishing/

Address of the bookmark: http://www.cbcb.umd.edu/finishing/

1. Must have basic understanding of molecular biology and Genomics.
2. Experience in application development or must have expertise in programming using either of Perl/Python.
3. Experience in statistical programming using R/Bioconductor/Matlab.
4. Strong concept in statistical and mathematical modelling.
5. Experience in designing and developing the bioinformatics pipeline.
6. Must have minimum 2+ years of hands on experience in NSG data analysis such as RNA-Seq,Exome-Seq ,Chip-Seq and downstream analysis.
7. Knowledge in WGS ,WES, Targeted re-sequencing,GWAS and population genomics will be preferred.
8. Must have experience working on opensource software/Framework and commercial software for NGS data analysis and reporting.
9. Should be aware of handling big data and guiding team members on multiple projects simultaneously.
10. Should have experience coordinating with different groups of clinical research scientist for various project requirements.
11. Ability to work as team as well as independently with minimal support.

More at http://www3.ocimumbio.com/

Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

Address of the bookmark: https://github.com/rrwick/Porechop

## Package Plan ##

environment location: /home/urbe/anaconda3

added / updated specs:
- salmon

The following packages will be downloaded:

package | build
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boost-1.64.0 | py36_4 331 KB conda-forge
jemalloc-5.1.0 | hfc679d8_0 8.2 MB conda-forge
boost-cpp-1.64.0 | 1 17.8 MB conda-forge
salmon-0.10.2 | 1 3.7 MB bioconda
conda-4.5.5 | py36_0 624 KB conda-forge
tbb-2018_20171205 | 0 1.2 MB conda-forge
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Total: 31.8 MB

The following NEW packages will be INSTALLED:

boost: 1.64.0-py36_4 conda-forge
boost-cpp: 1.64.0-1 conda-forge
jemalloc: 5.1.0-hfc679d8_0 conda-forge
salmon: 0.10.2-1 bioconda
tbb: 2018_20171205-0 conda-forge

The following packages will be UPDATED:

conda: 4.5.4-py36_0 conda-forge --> 4.5.5-py36_0 conda-forge

Proceed ([y]/n)? y

Downloading and Extracting Packages
boost-1.64.0 | 331 KB | ####################################################################################################################################### | 100%
jemalloc-5.1.0 | 8.2 MB | ####################################################################################################################################### | 100%
boost-cpp-1.64.0 | 17.8 MB | ####################################################################################################################################### | 100%
salmon-0.10.2 | 3.7 MB | ####################################################################################################################################### | 100%
conda-4.5.5 | 624 KB | ####################################################################################################################################### | 100%
tbb-2018_20171205 | 1.2 MB | ####################################################################################################################################### | 100%
Preparing transaction: done
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A list of programs for genotype and SNP calling :

SOAP2 http://soap.genomics.org.cn/index.html

Single-sample High-quality variant database (for example, dbSNP) Package for NGS data analysis, which includes a single individual genotype caller (SOAPsnp)

realSFS http://128.32.118.212/thorfinn/realSFS/

Single-sample Aligned reads Software for SNP and genotype calling using single individuals and allele frequencies. Site frequency spectrum (SFS) estimation

Samtools http://samtools.sourceforge.net/

Multi-sample Aligned reads Package for manipulation of NGS alignments, which includes a computation of genotype likelihoods (samtools) and SNP and genotype calling (bcftools)

GATK http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit Multi-sample Aligned reads Package for aligned NGS data analysis, which includes a SNP and genotype caller (Unifed Genotyper), SNP filtering (Variant Filtration) and SNP quality recalibration (Variant Recalibrator)

Beagle http://faculty.washington.edu/browning/beagle/beagle.html

Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation, phasing and association that includes a mode for genotype calling

IMPUTE2 http://mathgen.stats.ox.ac.uk/impute/impute_v2.html

Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation and phasing, including a mode for genotype calling. Requires fine-scale linkage map

QCall ftp://ftp.sanger.ac.uk/pub/rd/QCALL

Multi-sample LD ‘Feasible’ genealogies at a dense set of loci, genotype likelihoods Software for SNP and genotype calling, including a method for generating candidate SNPs without LD information (NLDA) and a method for incorporating LD information (LDA). The ‘feasible’ genealogies can be generated using Margarita (http://www.sanger.ac.uk/resources/software/margarita)

MaCH http://genome.sph.umich.edu/wiki/Thunder

Multi-sample LD Genotype likelihoods Software for SNP and genotype calling, including a method (GPT_Freq) for generating candidate SNPs without LD information and a method (thunder_glf_freq) for incorporating LD information