<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/41493?offset=740 https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links Thu, 13 Feb 2020 00:20:00 -0600 https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links <![CDATA[genomics public data links !]]> List of publically available databases on google server.

More at https://software.broadinstitute.org/gatk/download/bundle

ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/.

ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/

Address of the bookmark: https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1

]]> Jit https://bioinformaticsonline.com/opportunity/view/43227/project-associate-i-project-associate-ii-senior-project-associate-igib Thu, 05 Aug 2021 16:11:32 -0500 <![CDATA[Project Associate-I | Project Associate-II | Senior Project Associate @ IGIB]]> Experience in Next Generation Sequencing (NGS) application and interest in Genomics/ Clinical / Translational Applications. OR Good computational programming skills and deep interest in working on interface of Genomics and Clinical application.

Project Scientist-I
Experimental / Computation analysis experience in highthroughput genomics/ clinical application.

Project Manager
Experience in handling large biological projects involving high-throughput genomics/ clinical application.

Scientific Administrative Assistant
Lab Work.

More at https://vinodscaria.genomes.in/positionsopen

]]> https://bioinformaticsonline.com/bookmarks/view/43943/bioinformatics-tutorial Mon, 22 Aug 2022 23:56:22 -0500 https://bioinformaticsonline.com/bookmarks/view/43943/bioinformatics-tutorial <![CDATA[Bioinformatics Tutorial !]]> This site aims to be a useful resource for bioinformatics beginners. Feel free to jump right in with the section most relevant to you, and if you're not sure, then the place to start is definitely Unix

Address of the bookmark: https://astrobiomike.github.io/

]]> Neel https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

]]> Jit https://bioinformaticsonline.com/blog/view/43900/finding-a-mimicry-game-for-teaching-on-line-and-mentioned-general-resources Tue, 28 Jun 2022 07:32:05 -0500 https://bioinformaticsonline.com/blog/view/43900/finding-a-mimicry-game-for-teaching-on-line-and-mentioned-general-resources <![CDATA[Finding a mimicry game for teaching on-line and mentioned general resources]]> Mimicry and other resources Mimicry games: Great Heliconius game: http://heliconius.org/evolving_butterflies/ (See also https://royalsocietypublishing.org/doi/10.1098/rspb.2020.0014) Other one, a bit less friendly: https://ccl.northwestern.edu/netlogo/models/Mimicry Camouflage practical https://alexis-catherine.github.io/publication/natural-selection-and-camouflage/ (NetLogo also has one: https://ccl.northwestern.edu/netlogo/models/BugHuntCamouflage) Peppered moth game: https://askabiologist.asu.edu/peppered-moths-game/play.html General resources The always popular Populus: https://cbs.umn.edu/populus/overview Drift & Gene Flow https://cartwrig.ht/apps/genie/ (Cock van Oosterhout has a great ppt to lead students through this) See also https://cartwrig.ht/apps/redlynx/ https://demonstrations.wolfram.com/ReplicatorMutatorDynamicsWithThreeStrategies/ NetLogo: http://ccl.northwestern.edu/netlogo/models/index.cgi Population Genetics: https://www.radford.edu/~rsheehy/Gen_flash/popgen/ Evolution in general https://evolution.berkeley.edu/evolibrary/home.php Mitochondrial Eve: https://projects.ncsu.edu/cals/gn/ex/mit-eve.html Y chromosomes: https://projects.ncsu.edu/cals/gn/ex/y-chrom.html A professional online package from Michael Kasumovic: https://arludo.com/ a compilation of resources: https://planted.botany.org/index.php?P=Home Finally, Donald Forsdyke has some great on-line videos explaining evolutionary principles (occasionally in a fake Scottish accent): http://post.queensu.ca/~forsdyke/videolectures.htm

 

]]> Shruti Paniwala https://bioinformaticsonline.com/researchlabs/view/4873/vveks-lab Thu, 26 Sep 2013 11:11:39 -0500 <![CDATA[Vvek's Lab]]> 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

]]> https://bioinformaticsonline.com/researchlabs/view/25987/chekulaevalab Tue, 12 Jan 2016 02:32:03 -0600 <![CDATA[Chekulaevalab]]> Focusing on understanding the molecular mechanisms that regulate mRNA translation, localization and stability and role of non-coding RNAs in this process. Up to 90% of human DNA is estimated to be transcribed into so called non-coding RNAs that are not translated into proteins. Many of them act as potent modifiers of gene expression. miRNAs are a class of such short non-coding RNAs. They regulate expression of more than a half of eukaryotic genes, thus, affecting multiple biological processes, including cell proliferation, differentiation, apoptosis and senescence. Not surprisingly, miRNAs are involved in many human pathologies, including cancer and neurological disorders and hold great potential as drug targets, disease markers, as well as therapeutic agents.
Our lab is located at the Berlin Institute for Medical Systems Biology (BIMSB), a part of the Max Delbrück Center for Molecular Medicine (MDC).

http://www.chekulaevalab.org/

]]> https://bioinformaticsonline.com/bookmarks/view/39372/irnad-a-computational-tool-for-identifying-d-modification-sites-in-rna-sequence Thu, 16 May 2019 00:20:07 -0500 https://bioinformaticsonline.com/bookmarks/view/39372/irnad-a-computational-tool-for-identifying-d-modification-sites-in-rna-sequence <![CDATA[iRNAD: a computational tool for identifying D modification sites in RNA sequence]]> iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. 

http://lin-group.cn/server/iRNAD/

Address of the bookmark: http://lin-group.cn/server/iRNAD/

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/42033/seastar-systematic-evaluation-of-alternative-start-site-in-rna Thu, 13 Aug 2020 09:54:27 -0500 https://bioinformaticsonline.com/bookmarks/view/42033/seastar-systematic-evaluation-of-alternative-start-site-in-rna <![CDATA[SEASTAR: Systematic Evaluation of Alternative STArt site in RNA]]> SEASTAR (Systematic Evaluation of Alternative STArt site in RNA) is a software package for Transcription Start Site (TSS) identification and quantification using only RNA-seq data. It assembles novel TSSs based only on RNA-Seq data and merges them with known TSSs from a public database. This package enables high-quality TSS identification that is comparable to the highly sophisticated CAGE technology. This package is particularly useful for finding novel TSSs that contribute to transcriptome complexity along with identifying differential promoter utilization.

version 1.0.0 - updates several descriptions and tests. To achieve v0.9.4, one can visit https://github.com/zhyqin/SEASTAR-0.9.4 for download.

Address of the bookmark: https://github.com/Xinglab/SEASTAR

]]> BioStar https://bioinformaticsonline.com/news/view/44724/step-by-step-guide-to-detect-pirnas-using-bioinformatics Fri, 13 Dec 2024 11:41:46 -0600 https://bioinformaticsonline.com/news/view/44724/step-by-step-guide-to-detect-pirnas-using-bioinformatics <![CDATA[Step-by-Step Guide to Detect piRNAs Using Bioinformatics]]> 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

  1. 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.

  2. 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.

  3. 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.

  1. Reference Genome Preparation
    Download the genome assembly of your organism from databases like Ensembl, UCSC Genome Browser, or NCBI.

  2. 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.

  3. 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.

  1. 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

  2. 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.

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

    python ping_pong_overlap.py sorted_reads.bam

  2. 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.

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

    proTRAC.pl -s sorted_reads.bam -g genome.fa -o clusters

  2. 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.

  1. 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

  2. Enrichment Analysis
    Perform GO or KEGG enrichment analysis of target genes using tools like g:Profiler or DAVID.

Step 7: Validation and Visualization

  1. Validate piRNA Candidates
    Cross-check the identified piRNAs against known piRNA databases, such as piRBase or piRNAdb.

  2. 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

  1. Archive Data
    Submit sequencing data to public repositories like SRA or GEO with metadata specifying piRNA-related experiments.

  2. 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.

]]> Abhi