BOL: Related items

An Introduction to Applied Bioinformatics

Jit — Fri, 02 Mar 2018 04:26:38 -0600

IAB is primarily being developed by Greg Caporaso(GitHub/Twitter: @gregcaporaso) in the Caporaso Lab at Northern Arizona University. You can find information on the courses I teach on my teaching website and information on my research and lab on my lab website.

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

LRSDAY: Long-read Sequencing Data Analysis for Yeasts

Poonam Mahapatra — Mon, 26 Aug 2019 18:07:33 -0500

Long-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads.

Address of the bookmark: https://github.com/yjx1217/LRSDAY

SciLifeLab tutorial for bioinformatics analysis !

Jit — Tue, 17 Apr 2018 04:33:00 -0500

SciLifeLab is a national center for molecular biosciences with focus on health and environmental research.

Courses

Old courses (2012-2014)

BioJupies: Automatically Generates RNA-seq Data Analysis Notebooks

Rahul Nayak — Sun, 20 Dec 2020 11:43:45 -0600

With BioJupies you can produce in seconds a customized, reusable, and interactive report from your own raw or processed RNA-seq data through a simple user interface

BioJupies now supports user accounts! Sign in from the top right corner of the page for access to unlimited private notebooks, RNA-seq datasets and alignment jobs.

Address of the bookmark: https://amp.pharm.mssm.edu/biojupies/

Jvarkit : Java utilities for Bioinformatics

Jit — Fri, 08 Jun 2018 09:31:55 -0500

Collection of Java tool kits for bioinformatics works: Jvarkit : Java utilities for Bioinformatics

Address of the bookmark: http://lindenb.github.io/jvarkit/

PhyloHerb: Phylogenomic Analysis Pipeline for Herbarium Specimens

LEGE — Wed, 21 Feb 2024 06:15:13 -0600

What is PhyloHerb: PhyloHerb is a wrapper program to process genome skimming data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming.

Address of the bookmark: https://github.com/lmcai/PhyloHerb/

DEPARTMENT OF GENETICS, GENOMICS AND BIOINFORMATICS, National Biotechnology Development Agency, Nigeria

BioStar — Wed, 05 Sep 2018 10:48:25 -0500

The Genetics, Genomics & Bioinformatics Department (GBBD) at NABDA is unique, encompassing all facets of modern genetics and bioinformatics research. Trans-disciplinary research being conducted in our laboratories would lead to cures for human diseases; improvements to crop and livestock quality and yield; creation of new technologies with applications to medicine; agriculture; environment; and industry.

Our capacity building activities covers both general and specialized topics in translational genetics, and is designed to better acquaint scientists and clinicians with the tools and technologies of genetics and genomics.

OUR RESEARCH ACTIVITIES INCLUDE:

Biomedical Genetics: investigating genetic and environmental factors contributing to phenotypes with relevance to human health and disease.
Computation and Bioinformatics: develop new approaches for the management, analysis, and modelling of large, complex data sets.
Population and Quantitative Genetics: study of how genetic processes evolve to generate genetic variation in populations of organisms, and the effects on the patterning of variation within and between populations and specie, and
Genetic Engineering and Biotechnology: focuses on the research and innovation for industrial enzymes, biologics and biosimilars production.

https://www.h3abionet.org/nabda

Address of the bookmark: http://www.nabda.gov.ng/departments/genetics-genomics-and-bioinformatics

Step-by-Step Guide to Detect piRNAs Using Bioinformatics

Abhi — Fri, 13 Dec 2024 11:41:46 -0600

Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs that play crucial roles in silencing transposable elements and regulating gene expression, particularly in germline cells. Detecting piRNAs involves identifying their unique characteristics, such as size, sequence motifs, and association with Piwi proteins, from high-throughput RNA sequencing data.

This blog provides a comprehensive step-by-step guide to detect piRNAs using bioinformatics tools and workflows.

Step 1: Prepare Your Data

Obtain RNA Sequencing Data
Acquire raw small RNA-seq data in FASTQ format. Datasets can be sourced from repositories like NCBI SRA, EMBL-EBI, or specific small RNA sequencing projects.
Quality Control (QC)
Use FastQC to assess the quality of raw reads:

fastqc reads.fastq

Evaluate the per-base quality, adapter content, and overrepresented sequences.
Trimming and Adapter Removal
Use tools like Cutadapt or Trim Galore! to remove adapters and low-quality bases:

cutadapt -a TGGAATTCTCGGGTGCCAAGG -o trimmed_reads.fastq reads.fastq

Ensure the remaining reads are of high quality for downstream analysis.

Step 2: Map Reads to the Genome

Mapping reads to the reference genome is crucial for identifying piRNA loci.

Reference Genome Preparation
Download the genome assembly of your organism from databases like Ensembl, UCSC Genome Browser, or NCBI.
Align Reads
Use Bowtie or STAR for small RNA alignment:

bowtie -v 1 -k 1 --best genome_index trimmed_reads.fastq -S aligned_reads.sam
- -v 1: Allows one mismatch.
- -k 1: Reports the best alignment.
Convert SAM to BAM
Convert and sort alignments using SAMtools:

samtools view -Sb aligned_reads.sam | samtools sort -o sorted_reads.bam

Step 3: Identify Small RNAs

piRNAs are characterized by their size (24–32 nt) and strand bias.

Extract Reads by Size
Use tools like BEDtools or custom scripts to filter reads between 24 and 32 nt:

bedtools bamtofastq -i sorted_reads.bam -fq all_reads.fastq seqkit seq -m 24 -M 32 all_reads.fastq > piRNA_size_reads.fastq
Check for Sequence Bias
piRNAs often have a strong bias for a uridine at the 5’ end (1U bias). Use tools like WebLogo to visualize sequence motifs.

Step 4: Detect Ping-Pong Signature

The ping-pong amplification loop is a hallmark of piRNA biogenesis, characterized by a 10 nt overlap between piRNAs on opposite strands.

Generate Overlap Statistics
Use the piPipes tool or custom scripts to calculate overlap:

python ping_pong_overlap.py sorted_reads.bam
Visualize Overlap Distribution
Plot the distribution of overlaps to confirm the presence of the 10 nt ping-pong signature.

Step 5: Annotate piRNA Clusters

piRNAs are often generated from genomic clusters.

Cluster Identification
Use tools like proTRAC or PIRANHA to identify piRNA-producing clusters:

proTRAC.pl -s sorted_reads.bam -g genome.fa -o clusters
Annotate Genomic Regions
Annotate the identified clusters using gene annotation files (GTF/GFF). Tools like BEDtools intersect can help associate piRNA clusters with genes or transposable elements:

bedtools intersect -a clusters.bed -b genome_annotation.gtf > annotated_clusters.bed

Step 6: Functional Analysis

Functional analysis of piRNAs can uncover their targets and regulatory roles.

Predict piRNA Targets
Use tools like IntaRNA or RNAhybrid to predict interactions between piRNAs and potential target mRNAs:

RNAhybrid -t target_transcripts.fa -q piRNAs.fa > piRNA_targets.txt
Enrichment Analysis
Perform GO or KEGG enrichment analysis of target genes using tools like g:Profiler or DAVID.

Step 7: Validation and Visualization

Validate piRNA Candidates
Cross-check the identified piRNAs against known piRNA databases, such as piRBase or piRNAdb.
Visualize Results
- Use IGV (Integrative Genomics Viewer) to visualize piRNA alignment and clusters on the genome.
- Generate heatmaps or circos plots to present piRNA distributions.

Step 8: Share and Publish Findings

Archive Data
Submit sequencing data to public repositories like SRA or GEO with metadata specifying piRNA-related experiments.
Publish Results
Share findings in journals or conferences, emphasizing novel piRNA candidates, target genes, or regulatory mechanisms.

Conclusion

Detecting piRNAs involves a combination of computational and analytical methods to identify these unique small RNAs and their roles in gene regulation and transposable element suppression. By following this step-by-step guide, you can confidently navigate the complexities of piRNA detection and contribute to the growing understanding of their biological significance.

Senior Bioinformatics Scientist @ Strand Life Sciences -- Bangalore, India

Wed, 02 Jan 2019 09:23:49 -0600

RESPONSIBILITIES
The candidate is expected to work on a variety of projects related to analysis of data from NGS, Mass Spectrometry, Flow Cytometry and other related modalities. The position expects hands-on work and a strong eye for detail. The candidate will be able to contribute to impactful work spanning patient care, clinical research, and new assay and method development.
REQUIREMENTS
A PhD in a quantitative field (statistics, math, bioinformatics, computer science, physics or similar) and work experience or post-doc experience handling high throughout genomics data.
PREFERENCES
Experience in working in inter-disciplinary groups and ability to author research publications are additional desired qualities.
LOCALE
The position is in Bangalore and reports to the Chief Scientific Officer.
HOW TO APPLY
Write to ramesh[at]strandls.com.

Alignment-free sequence comparison tools available for next-generation sequencing data analysis

Abhimanyu Singh — Tue, 07 Nov 2017 05:33:33 -0600

kallisto

Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)

Software (C++)

https://pachterlab.github.io/kallisto/

Sailfish

Estimation of isoform abundances from reference sequences and RNA-seq data (k-mer based)

Software (C++)

http://www.cs.cmu.edu/~ckingsf/software/sailfish/

Salmon

Quantification of the expression of transcripts using RNA-seq data (uses k-mers)

https://combine-lab.github.io/salmon/

RNA-Skim

RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses sig-mers, a special type of k-mers)

Software (C++)

http://www.csbio.unc.edu/rs/

Variant calling

ChimeRScope

Fusion transcript prediction using gene k-mers profiles of the RNA-seq paired-end reads

Software (Java)

https://github.com/ChimeRScope/ChimeRScope/wiki

FastGT

Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique k-mers

Software (C)

https://github.com/bioinfo-ut/GenomeTester4/

Phy-Mer

Reference-independent mitochondrial haplogroup classifier from NGS data (k-mer based)

Software (Python)

https://github.com/danielnavarrogomez/phy-mer

LAVA

Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (k-mer based)

Software (C)

http://lava.csail.mit.edu/

MICADo

Detection of mutations in targeted third-generation NGS data (can distinguish patients’ specific mutations; algorithm uses k-mers and is based on colored de Bruijn graphs)

Software (Python)

http://github.com/cbib/MICADo

General mapper

Minimap

Lightweight and fast read mapper and read overlap detector (uses the concept of “minimazers”, a special type of k-mers)

Software (C)

https://github.com/lh3/minimap

Assembly

De novo genome assembly

MHAP

Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash

Software (Java)

https://github.com/marbl/MHAP

Miniasm

Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)

Software (C)

https://github.com/lh3/miniasm

LINKS

Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)

Software (Perl)

https://github.com/warrenlr/LINKS/

Read clustering

afcluster

Clustering of reads from different genes and different species based on k-mer counts

Software (C++)

https://github.com/luscinius/afcluster

QCluster

Clustering of reads with alignment-free measures (k-mer based) and quality values

Software (C++)

http://www.dei.unipd.it/~ciompin/main/qcluster.html

Reads error correction

Lighter

Correction of sequencing errors in raw, whole genome sequencing reads (k-mer based)

Software (C++)

https://github.com/mourisl/Lighter

QuorUM

Error corrector for Illumina reads using k-mers

Software (C++)

https://github.com/gmarcais/Quorum

Trowel

Software (C++)

https://sourceforge.net/projects/trowel-ec/

Metagenomics

Assembly-free phylogenomics

AAF

Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (k-mer based)

Software (Python)

https://github.com/fanhuan/AAF

kSNP v3

Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on k-mer analysis)

Software (C)

https://sourceforge.net/projects/ksnp/files/

NGS-MC

Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2 S under different Markov chain models (using k-words)

R package

http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html

Species identification/taxonomic profiling

CLARK

Taxonomic classification of metagenomic reads to known bacterial genomes using k-mer search and LCA assignment

Software (C++)

http://clark.cs.ucr.edu/

FOCUS

Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)

Web service Software (Python)

http://edwards.sdsu.edu/FOCUS/

GSM

Estimation of abundances of microbial genomes in metagenomic samples (k-mer based)

Software (Go)

https://github.com/pdtrang/GSM

Mash

Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)

Software (C++)

https://github.com/marbl/mash

Kraken

Taxonomic assignment in metagenome analysis by exact k-mer search; LCA assignment of short reads based on a comprehensive sequence database

Software (C++)

https://ccb.jhu.edu/software/kraken/

LMAT

Assignment of taxonomic labels to reads by k-mers searches in precomputed database

Software (C++/Python)

https://sourceforge.net/projects/lmat/

stringMLST

k-mer-based tool for MLST directly from the genome sequencing reads

Software (Python)

http://jordan.biology.gatech.edu/page/software/stringMLST

Taxonomer

k-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples

Web service

http://taxonomer.iobio.io/

Other

d2-tools

Word-based (k-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads

Software (Python/R)

https://code.google.com/p/d2-tools/

VirHostMatcher

Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)

Software (C++)

https://github.com/jessieren/VirHostMatcher

MetaFast

Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray–Curtis dissimilarity measure

Software (Java)

https://github.com/ctlab/metafast

BOL: Related items

An Introduction to Applied Bioinformatics

LRSDAY: Long-read Sequencing Data Analysis for Yeasts

SciLifeLab tutorial for bioinformatics analysis !

Courses

Metagenomics Workshop

Introduction to Bioinformatics Using NGS Data

Introduction to Genome Annotation

De Novo Genome Assembly

RNA-seq course

R Programming Foundations for Life Scientists

Single cell RNA sequencing analysis

BioJupies: Automatically Generates RNA-seq Data Analysis Notebooks

Jvarkit : Java utilities for Bioinformatics

PhyloHerb: Phylogenomic Analysis Pipeline for Herbarium Specimens

DEPARTMENT OF GENETICS, GENOMICS AND BIOINFORMATICS, National Biotechnology Development Agency, Nigeria

Step-by-Step Guide to Detect piRNAs Using Bioinformatics

Step 1: Prepare Your Data

Step 2: Map Reads to the Genome

Step 3: Identify Small RNAs

Step 4: Detect Ping-Pong Signature

Step 5: Annotate piRNA Clusters

Step 6: Functional Analysis

Step 7: Validation and Visualization

Step 8: Share and Publish Findings

Conclusion

Senior Bioinformatics Scientist @ Strand Life Sciences -- Bangalore, India

Alignment-free sequence comparison tools available for next-generation sequencing data analysis