Source: http://www.nipgr.res.in/careers/vacancies_latest.php#
Form at http://www.nipgr.res.in/files/careers/format_RA_JRF_LA.doc

Research Associate /01 Post

Essential: Ph.D. in Bioinformatics or 03 years research experience after Post Graduation in Bioinformatics with at least one research paper in Science Citation Indexed (SCI) journals.

Desirable: The candidate should have at least 1st Division during Graduation and Post Graduation. Experience in assembly/ analysis/ annotation of genomic/transcriptomic data generated on next generation sequencing platforms and working knowledge on different genomic softwares. Publications in Relevant Field.

Pay Scale : Rs. 36,000/- +20% HRA

Age: 40 years for male and 45 years for female candidates, as on the date of interview

Junior Research Fellow/ 01

Essential: Master Degree in Biotechnology/Life Science with Specialization in Molecular Biology with NET qualification.

Desirable: Research Experience in Molecular Biology. 1st Division during Graduation as well as Post Graduation. Publications in Relevant Field.

Pay Scale: Rs. 25,000/-+ 20% HRA for 1st and 2nd year and Rs. 28,000/-+ 20% HRA for 3rd year

Age: 35 years for male and 40 years for female candidates, as on the date of interview.
How to apply
A walk-in-interview will be held on 26.07.2016 at 10:00 hrs. at ICAR-National Bureau of Fish Genetic Resources, Lucknow.

More at http://www.nbfgr.res.in/Recruitments.aspx

The new features include

(i) building gene-based and supermatrix-based phylogenies using a single command,

(ii) testing and visualizing evolutionary models,

(iii) calculating distances between trees of different size or including duplications, and

(iv) providing seamless integration with the NCBI taxonomy database.

ETE is freely available at http://etetoolkit.org

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

EMBASSY applications are described in separate documentation for each package.

Applications in the current release

Address of the bookmark: http://emboss.sourceforge.net/apps/release/6.6/emboss/apps/

Fellowship: Rs. 18,000/- per month (fixed)

Duration : 2 (Two) years and may be extended
depending on status of the project

Age Limit: Candidates should not be more than 32 years of
age in case of SRF and 28 years of age in case of JRF and TA. Upper age limit may be relaxed up to 5
years in the case of candidate belonging to SC/ ST/ OBC/ Women/ Differently abled.

How to Apply:
Interested candidates may send their application on plain paper by post along with his/her educational
qualifications, research experience certificates (for SRF), 02 copies of recent passport/stamp size photographs
and contact phone number to Professor D.K Bhattacharyya, Principal Investigator, Department of Computer
Science & Engineering, Tezpur University, Napaam – 784 028, or mail it to dkb@tezu.ernet.in
(or to smh@tezu.ernet.in) within 15 days of publication of this advertisement.

No TA/DA shall
be paid for attending the interview.

For more details: http://www.tezu.ernet.in/ProjectWalkin/Advt-DoRD-CSE-DKB-20-225-6779-A.pdf

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

WALK-IN INTERVIEW

JIP/PSM/INDO-US TB/ 2016/

Walk-in-interview for the following vacant posts funded by Department of Biotechnology, Govt.of India for the project entitled “Biomarkers for Risk of Tuberculosis and for Tuberculosis Treatment Failure and Relapse” in the Department of Preventive & Social Medicine, JIPMER, Puducherry.

3. Technical Assistant

MCA/ MSc in Biostatistics/ MSc in Computational Biology from any recognized University @ Rs.23,220 1

Interested candidates may attend the walk-in interview with written screening test on 07, September 2016 at 9.30 A.M in the Dept. of Preventive and Social Medicine, IV Floor, Administrative Block, JIPMER.

The applicants are requested to bring the filled in application form and bio-data with original certificates for verification.

More Info: http://jipmer.edu.in/wp-content/uploads/2016/09/RECRUITEMENTsite-protocol-7.9.2016.pdf

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/

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

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.