BOL: Related items

chromoMap-An R package for Interactive visualization and mapping of human chromosomes

Rahul Nayak — Mon, 25 Jun 2018 17:22:24 -0500

chromoMap is an R package that provides interactive, configurable and elegant graphics visualization of the human chromosomes allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the enormous size of the chromosomes, it is impractical to visualize each element on the same plot. But chromoMap plots provide a magnified view for each of chromosome location to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order (You will see in the demos below). Not ony that, the plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

https://cran.r-project.org/web/packages/chromoMap/index.html

Scientists map 17,294 proteins produced in human body

Jit — Thu, 29 May 2014 01:57:55 -0500

Indian scientists missed the genomic profiling bus, but they've more than made up for it by creating the first human proteome map which is an extension of the genomic study. Till now, here is no direct equivalent for the human proteome. But recently two groups present mass spectrometry-based analysis of human tissues, body fluids and cells mapping the large majority of the human proteome.

The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.

The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".

The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.

Reference:

http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html

http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme

http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.

https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB

MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping

Neel — Fri, 20 May 2016 18:53:49 -0500

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery.

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581

Vvek's Lab

Thu, 26 Sep 2013 11:11:39 -0500

Broad Area of Research: RNA biology (microRNA, lncRNA), Stem cells, Functional genomics, Epigenomics and Cancer

RNAs, especially non-coding RNAs (such as microRNA, long ncRNAs) are recently identified to be very abundant in mammalian organisms and play some key roles in gene expression regulation, gene silencing, and also implicated in disease progression, stem cell pluripotency etc. Current research activities of our lab include analysis of expression pattern of ncRNAs by microarray and next-gen sequencing data and understanding the role of miRNAs or other regulatory RNAs in various diseases, especially cancer and validation by reporter assays (renilla/luciferase) and other experimental tools.

More @ http://vvekslab.in/index.html

Roderic Guigó Lab

Mon, 01 Sep 2014 17:13:00 -0500

Research in our group focuses on the investigation of the signals involved in gene specification in genomic sequences (promoter elements, splice sites, translation initiation sites, etc…). We are interested both in the mechanism of their recognition and processing, and in their evolution. In addition, but related to this basic component of our research, our group is also involved in the development of software for gene prediction and annotation in genomic sequences. Our group also actively participates in the analysis of many eukaryotic genomes and it in involved in the NIH-funded ENCODE project. Furthermore we are members of two large cancer-studies consortia (chronic lymphocytic leukemia "CLL" and Breast Cancer -Hospital del Mar/CRG/Roche-).

More at http://big.crg.cat/computational_biology_of_rna_processing

Hoffman Lab

Tue, 12 Jan 2016 02:47:41 -0600

They develop machine learning techniques to better understand chromatin biology. These models and algorithms transform high-dimensional functional genomics data into interpretable patterns and lead to new biological insight.

https://www.pmgenomics.ca/hoffmanlab/

Tools for RNA classification

Abhi — Tue, 08 Nov 2022 03:39:11 -0600

barrnap - https://github.com/tseemann/barrnap

CPAT - https://github.com/liguowang/cpat, http://lilab.research.bcm.edu/ (web server)

CPC2 - https://github.com/gao-lab/CPC2_standalone, http://cpc2.gao-lab.org/ (web server)

Infernal - http://eddylab.org/infernal/, https://github.com/EddyRivasLab/infernal

NCBI RefSeq - https://www.ncbi.nlm.nih.gov/refseq/

Rfam - http://rfam.xfam.org/, https://docs.rfam.org/en/latest/index.html

SILVA - https://www.arb-silva.de/

RNAmmer - http://www.cbs.dtu.dk/services/RNAmmer/ (web server, standalone download link)

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.

Does anyone have Nanopore latest updates?

Poonam Mahapatra — Mon, 12 Aug 2013 12:19:29 -0500

There was a lot of buzz about Oxford Nanopore Technologies® is developing the GridION™ system and miniaturised MinION™ device. These are a new generation of electronic molecular analysis system for use in scientific research, personalised medicine, crop science, security/defence and more. The platform technology uses nanopores to analyse single molecules including DNA/RNA and proteins. With a broad patent portfolio, the Oxford Nanopore pipeline includes biological nanopores and solid-state nanopores.

Is this available, or still under trial mode?

https://www.nanoporetech.com/

https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system