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.

https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw377

Address of the bookmark: http://amp.pharm.mssm.edu/Enrichr/

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.

• exteremely fast (17 CPU hours for whole Human x Mouse genome (with 40 nodes: 35 wall minutes), or 8 mammals in 21 CPU hours (42 wall minutes))
• scalable (Arbitrarily parallelizable across multiple nodes using MPI)
• memory efficient. (Even a single node with 16GB of ram can handle over 1Gbp of sequence)
• unlimited by pattern length or selection
• repeat tolerant

Address of the bookmark: http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki

Address of the bookmark: http://seq.crg.es/download/software/Miro/

List of analysis tools developed for Oxford Nanopore data

BWA
Fast nanopore data tuned alignment tool
https://github.com/lh3/bwa

GraphMap
Mapper for long and error-prone reads
https://github.com/isovic/graphmap

LAST
Nanopore tuned alignment tool
http://last.cbrc.jp/

LINKS
Software tool for long read scaffolding
https://github.com/warrenlr/LINKS/

marginAlign
Tools to align nanopore reads to a reference
https://github.com/benedictpaten/marginAlign

minoTour
Real time analysis tools
http://minotour.nottingham.ac.uk/

nanoCORR
Error-correction tool for nanopore sequence data
https://github.com/jgurtowski/nanocorr

NanoOK
Software for nanopore data, quality and error profiles
https://documentation.tgac.ac.uk/display/NANOOK/NanoOK

Nanopolish
Nanopore analysis and genome assembly software
https://github.com/jts/nanopolish

nanopore
Variant-detection tool for nanopore sequence data
https://github.com/mitenjain/nanopore

Nanocorrect
Error-correction tool for nanopore sequence data
https://github.com/jts/nanocorrect/

npReader
Real-time conversion and analysis of nanopore reads
https://github.com/mdcao/npReader

poRe
Tool for analyzing and visualizing nanopore data
https://sourceforge.net/p/rpore/wiki/Home/

PoreSeq
Error-correction and variant-calling software
https://github.com/tszalay/poreseq

Poretools
Nanopore sequence analysis and visualization software
https://github.com/arq5x/poretools

SSPACE-LongRead
Genome scaffolding tool
http://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE-longread

SMIS
Genome scaffolding tool
https://sourceforge.net/projects/phusion2/files/smis/

List of assemblers for Oxford Nanopore MinION long reads

LQS
DALIGNER, Celera OLC Nanocorrect,
Nanopolish corrector
https://github.com/jts/nanopolish

PBcR
HGAP or BLASR, Celera OLC
PBcR corrector
http://wgs-assembler.sourceforge.net/wiki/index.php/PBcR
–
Canu
MHAP, Celera OLC
Canu corrector
https://github.com/marbl/canu

Falcon
String graph, Celera OLC
Falcon corrector
https://github.com/PacificBiosciences/falcon

Miniasm
OLC
https://github.com/lh3/miniasm

ra-integrate
OLC
https://github.com/mariokostelac/ra-integrate/

ALLPATHS-LG
de Bruijn graph
ALLPATHS-L corrector
https://www.broadinstitute.org/software/allpaths-lg/blog/?page_id=12

SPAdes
de Bruijn graph
SPAdes corrector
http://bioinf.spbau.ru/spades

If you are interested in gene prediction, have a look at GenomeThreader.

Address of the bookmark: http://genometools.org/

https://function.princeton.edu/