BOL: Related items

RNA-seq workflow: gene-level exploratory analysis and differential expression

Jit — Sat, 09 Oct 2021 07:59:23 -0500

Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were quantified to the reference transcripts, and prepare gene-level count datasets for downstream analysis. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

Address of the bookmark: http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

Exploring RNA Sequence Analysis: Tools for Every Bioinformatician

Neel — Fri, 13 Dec 2024 04:03:04 -0600

RNA sequence analysis has become an essential part of modern biological research. From RNA-seq pipelines to specialized tools for specific RNA types, here's a comprehensive guide to tools you can use to make sense of RNA data.

1. RNA-Seq Analysis Pipelines

RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:

FASTQC: For quality control of raw RNA-seq reads.
Trimmomatic: For trimming and filtering RNA-seq reads.
HISAT2/STAR: High-performance aligners for RNA-seq reads.
FeatureCounts: For quantifying gene expression.
DESeq2/EdgeR: For differential expression analysis.

2. Transcriptome Assembly and Annotation

For analyzing transcriptomes from non-model organisms or assembling novel transcripts:

Trinity: For de novo transcriptome assembly.
StringTie: For transcript assembly and quantification from RNA-seq alignments.
TransDecoder: To predict coding regions within assembled transcripts.
TAU: Tools for annotating non-coding and coding RNAs.

3. Exploring Non-Coding RNA (ncRNA)

Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:

Infernal: For identifying ncRNA sequences based on covariance models.
Rfam: Database and tools for ncRNA families.
miRDeep: For identifying microRNAs in RNA-seq datasets.

4. RNA Structure and Motif Analysis

Structural biology of RNA helps in understanding its function:

RNAfold (ViennaRNA): Predicts secondary structures from RNA sequences.
RNAstructure: Tools for RNA secondary structure prediction and analysis.
MEME Suite: For identifying motifs in RNA sequences.
IntaRNA: For RNA-RNA interaction prediction.

5. RNA Editing and Modifications

Epitranscriptomics is a growing field focusing on RNA modifications:

REDItools: For RNA editing analysis.
m6Aboost: For identifying m6A modifications in RNA.

6. Long-Read RNA Sequencing Analysis

Long-read technologies like Nanopore and PacBio are transforming RNA research:

FLAIR: For isoform-level analysis of long-read RNA-seq data.
NanoMod: For detecting modifications in RNA from Nanopore sequencing.

7. RNA-Protein Interactions

To study RNA-protein interactions and complexes:

RBPmap: For identifying RNA-binding protein motifs.
PARalyzer: For analyzing PAR-CLIP data.

8. Functional Enrichment Analysis

Understanding biological functions and pathways from RNA-seq data:

getENRICH: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).
ClusterProfiler: For GO and KEGG pathway enrichment analysis.

9. Visualization and Data Sharing

Presenting and sharing RNA sequence analysis results effectively:

IGV: Genome browser for visualizing RNA-seq alignments.
Circos: Circular visualization of RNA-seq data.
DashBio: A Python library for creating bioinformatics visualizations.

Conclusion

The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you’re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.

RNA-Seq De novo Assembly Using Trinity

Surabhi Chaudhary — Wed, 23 Mar 2016 05:53:46 -0500

Trinity, developed at the Broad Institute and the Hebrew University of Jerusalem, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so:

Inchworm assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.
Chrysalis clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.
Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes.

More at https://github.com/trinityrnaseq/trinityrnaseq/wiki

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Download Trinity here.

Build Trinity by typing 'make' in the base installation directory.

Assemble RNA-Seq data like so:

 Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G

Find assembled transcripts as: 'trinity_out_dir/Trinity.fasta'

Address of the bookmark: https://github.com/trinityrnaseq/trinityrnaseq/wiki

PEAR: a fast and accurate Illumina Paired-End reAd mergeR

Jit — Wed, 06 Apr 2016 13:27:23 -0500

PEAR is an ultrafast, memory-efficient and highly accurate pair-end read merger. It is fully parallelized and can run with as low as just a few kilobytes of memory.

PEAR evaluates all possible paired-end read overlaps and without requiring the target fragment size as input. In addition, it implements a statistical test for minimizing false-positive results. Together with a highly optimized implementation, it can merge millions of paired end reads within a couple of minutes on a standard desktop computer.

More at http://www.exelixis-lab.org/web/software/pear

Paper: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3933873/

Address of the bookmark: http://www.exelixis-lab.org/web/software/pear

SCALCE

Surabhi Chaudhary — Fri, 15 Apr 2016 05:09:51 -0500

SCALCE (/skeɪlz/, a.k.a. boosting Sequence Compression Algorithms using Locally ConsistentEncoding) is a tool for compressing FASTQ files. It is designed specifically for the Illumina-generated FASTQ files, but supports any valid FASTQ with consistent read lengths.

More at http://sfu-compbio.github.io/scalce/

Address of the bookmark: http://sfu-compbio.github.io/scalce/

SPAdes

Abhimanyu Singh — Tue, 19 Apr 2016 08:37:08 -0500

SPAdes – St. Petersburg genome assembler – is intended for both standard isolates and single-cell MDA bacteria assemblies. This manual will help you to install and run SPAdes. SPAdes version 3.7.1 was released under GPLv2 on March 8, 2016 and can be downloaded from http://bioinf.spbau.ru/en/spades.

Manual at http://spades.bioinf.spbau.ru/release3.7.1/manual.html

Address of the bookmark: http://bioinf.spbau.ru/spades

BUSCO: Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs

Anjana — Tue, 10 May 2016 07:46:24 -0500

High-throughput genomics has revolutionized biological research, however, while the number of sequenced genomes grows by the day, quality assessment of the resulting assembled sequences remains complicated and mostly limited to technical measures like N50.
BUSCO provides measures for quantitative assessment of genome assembly, gene set, and transcriptome completeness based on evolutionarily informed expectations of gene content from near-universal single-copy orthologs selected from OrthoDB.
BUSCO assessments are implemented in open-source software, with comprehensive lineage-specific sets of Benchmarking Universal Single-Copy Orthologs for arthropods, vertebrates, metazoans, fungi, eukaryotes, and bacteria.
These conserved orthologs are ideal candidates for large-scale phylogenomics studies, and the annotated BUSCO gene models built during genome assessments provide a comprehensive gene predictor training set for use as part of genome annotation pipelines.
BUSCO assessments offer intuitive metrics, based on evolutionarily informed expectations of gene content from hundreds of species, to gauge completeness of rapidly accumulating genomic data and satisfy an Iberian's quest for quality - "Busco calidad/qualidade".

Address of the bookmark: http://busco.ezlab.org/

mrFAST: Micro Read Fast Alignment Search Tool

Neel — Tue, 26 Apr 2016 03:50:06 -0500

mrFAST is a read mapper that is designed to map short reads to reference genome with a special emphasis on the discovery of structural variation and segmental duplications. mrFAST maps short reads with respect to user defined error threshold, including indels up to 4+4 bp. This manual, describes how to choose the parameters and tune mrFAST with respect to the library settings. mrFAST is designed to find 'all' mappings for a given set of reads, however it can return one "best" map location if the relevant parameter is invoked.

More at http://mrfast.sourceforge.net/manual.html

Address of the bookmark: http://mrfast.sourceforge.net/manual.html

Sr.Bioinformatics Analyst (NGS) @ OcimumBio

Mon, 02 May 2016 04:41:22 -0500

Sr.Bioinformatics Analyst (NGS)

Experience Required: 3-5 years of experience
No of Positions : Multiple
Qualifications: Candidates with minimum qualification as M.Sc Bioinformatics with 3-5 years of experience in Life sciences R&D or Pharma Industry.
Ph.D candidates with research experience in Bioinformatics with publications in International journal and minimum 2 years of industry experience in clinical genomics will be preferred for this position.

Requirement:

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.
More at http://www.ocimumbio.com/careers1/
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.

segemehl

Anjana — Tue, 10 May 2016 08:10:15 -0500

segemehl is a software to map short sequencer reads to reference genomes. Unlike other methods, segemehl is able to detect not only mismatches but also insertions and deletions. Furthermore, segemehl is not limited to a specific read length and is able to map primer- or polyadenylation contaminated reads correctly. segemehl implements a matching strategy based on enhanced suffix arrays (ESA).

More at http://www.bioinf.uni-leipzig.de/Software/segemehl/

Manual http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_manual_0_1_7.pdf

Address of the bookmark: http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html