• Generate a cell x gene or cell x transcript equivalence class count matrix
• Perform RNA velocity and single-nuclei RNA-seq analsis
• Quantify data from numerous technologies such as 10x, inDrops, and Dropseq.
• Customize workflows for new technologies and protocols.
• Process feature barcoding data such as CITE-seq, REAP-seq, MULTI-seq, Clicktags, and Perturb-seq.
• Obtain QC reports from single-cell RNA-seq data

The kallisto | bustools workflow is described in:

Páll Melsted*, A. Sina Booeshaghi*, Lauren Liu, Fan Gao, Lambda Lu, Kyung Hoi (Joseph) Min, Eduardo da Veiga Beltrame, Kristján Eldjárn Hjörleifsson, Jase Gehring & Lior Pachter† Modular and efficient pre-processing of single-cell RNA-seq, Nature Biotechnology (2021).

Documentation and tutorials for the kallisto bustools workflow are available at http://pachterlab.github.io/kallistobustools. 

https://www.nature.com/articles/s41587-021-00870-2

Address of the bookmark: https://pachterlab.github.io/kallistobustools/

Tool link:

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

Address of the bookmark: http://www.genengnews.com/gen-news-highlights/lightweight-algorithms-sail-through-rna-sequencing-data/81249765/

ORLANDO, USA, Oct 17, 2017/ PRNewswire/

Strand NGS now supports large scale RNA- and small-RNA-Seq and Unique Molecular Identifiers (UMIs) for DNA-, RNA-, and small-RNA-Seq.

Strand Life Sciences announced the latest version release of its bioinformatics flagship product, Strand NGS, at the Annual Meeting of the American Society of Human Genetics today. Two major themes in Strand NGS v3.1 address recent challenges in next generation sequencing (NGS).

The first theme is large-scale RNA-Seq data analysis. Current cross-cohort RNA- and small-RNA-Seq studies span tens of replicates and batches across hundreds of samples, sometimes conducted across several different institutions. For such studies, Strand NGS v3.1 includes confounding variable analysis to eliminate technical effects, including batch effects; the t-SNE plot; profile and heat-map plots of gene-body coverage; and several other notable visual enhancements.

The second new feature is support for Unique Molecular Identifiers, or UMIs, for DNA-, RNA- and small-RNA-Seq. UMI support in Strand NGS is end-to-end, spanning alignment to variant calling in DNA-Seq, and alignment to quantification in RNA- and small-RNA-Seq. The Bioo Scientific, Qiagen, and Rubicon UMI protocols are natively supported, and an intuitive interface allows the specification of custom UMI protocols.

“For liquid biopsies and low-grade FFPE samples, UMI support in DNA-Seq enables the detection of somatic variants at low concentrations. In RNA-Seq, large-scale and UMI support can be used in single-cell-based studies that reveal tumor-cell heterogeneity, even at low concentrations”, says Dr. Vamsi Veeramachaneni, Chief Scientific Officer, Strand Life Sciences.

“At Strand, we are continuously working towards improving the accuracy and efficiency of NGS data analysis. Customers can look forward to Strand NGS becoming available on the cloud in the near future”, says Dr. Ramesh Hariharan, Chief Executive Officer, Strand Life Sciences.

Visit Strand Life Sciences at ASHG booth #1017 to know more about Strand NGS v3.1 and other products and service offerings from Strand Life Sciences. Click here to access detailed agenda and v3.1 release notes.

About Strand Life Sciences

Strand Life Sciences is a premier life science informatics innovation company. Founded in 2000, Strand is a leader in technology innovations for healthcare using genomics. By enhancing sequence-based diagnostics and clinical genomic data interpretation using a strong foundation of computational, scientific, and medical expertise, Strand is bringing individualized medicine to the world. To know more, visit www.strandls.com

Address of the bookmark: http://www.strand-ngs.com/strand-announce-strandngss-v31

Address of the bookmark: https://ccb.jhu.edu/software/stringtie/

What Are Kallisto and Salmon?

At their core, both Kallisto and Salmon are tools for quantifying transcript abundance from RNA-Seq reads. They bypass traditional alignment-based methods, replacing them with pseudoalignment or quasi-mapping, which drastically speeds up the process.

• Kallisto was developed by Lior Pachter’s lab and introduced the concept of pseudoalignment using a de Bruijn graph.
• Salmon, developed by Rob Patro’s group, builds on this idea with quasi-mapping and offers additional features like advanced bias correction.

Head-to-Head Comparison

1. Algorithm

• Kallisto uses pseudoalignment, focusing on matching k-mers from reads to a transcriptome index.
• Salmon uses quasi-mapping, which adds more flexibility and can also work with aligned reads (BAM files).

2. Input and Flexibility

• Kallisto works with raw FASTQ reads and requires a custom transcriptome index.
• Salmon accepts FASTQ or pre-aligned BAM files, giving you more workflow options.

3. Bias Correction

One of Salmon’s major advantages is its sophisticated bias correction system. It corrects for:

• Sequence-specific bias
• Positional bias
• GC-content bias

Kallisto offers basic sequence bias correction but lacks the comprehensive models found in Salmon.

4. Speed and Resources

• Kallisto is blazing fast and slightly more memory-efficient.
• Salmon is still very fast, but the added features can come at a small computational cost.

5. Output and Downstream Analysis

• Both tools provide transcript-level quantifications and support bootstrapping for variance estimation.
• Salmon can also summarize counts at the gene level if provided with a mapping file (--geneMap).
• Kallisto integrates seamlessly with Sleuth for differential expression analysis.
• Salmon works well with tximport, DESeq2, edgeR, and other Bioconductor tools.

Choosing the Right Tool

Example Command Lines

Kallisto (paired-end):

kallisto quant -i transcriptome.idx -o output -b 100 sample_R1.fastq sample_R2.fastq

Salmon (paired-end, bias correction):

salmon quant -i salmon_index -l A -1 sample_R1.fastq -2 sample_R2.fastq \
  -p 8 --validateMappings --seqBias --gcBias -o output

Conclusion

Both Kallisto and Salmon are exceptional tools that have transformed RNA-Seq analysis. Your choice largely depends on your priorities—whether it's speed, accuracy, flexibility, or compatibility with downstream tools.

For many users, Salmon offers a more complete and flexible solution, especially when bias correction and gene-level outputs are essential. However, Kallisto remains a favorite for quick, accurate quantification, especially when paired with the Sleuth pipeline.

Address of the bookmark: http://hibberdlab.com/transrate/index.html

Sponsored/CSIR Networked Project:
(i) Genomics and Informatics Solutions for Integrating Biology (GENESIS)" [BSC-0121] (up to March, 2017).
(ii) Profiling and characterization of early phase differential mi-RNA (s) responsible for downstream developmen of insulin resistance in hMSC derived adipocytes. (GAP-0188) [up to 31.03.2018].

Position: Project Fellow (2 position)
Age : 28 years as on 18.12.15
Salary : Rs.12,000/- P.M.
Rs.14,000/- P.M.
as per the funds provisions in the respective projects.
Eligibility Criteria : 1st Class B. Tech. in Bioinformatics/ Computational Biology
OR
M.Sc. in Bioinformatics/ Computational Biology with 55% marks
OR
M.Tech. in Bioinformatics/ Computational Biology with 55% marks
OR
M.Sc. in any field of Life Science with Diploma in Bioinformatics

Selection Procedure : Walk In Interview

Date : 18 Dec , 2015
Time : 10:00 A.M.
Venue : CSIR-IHBT Palampur (H.P.)

Junior Project Fellow Jobs opportunity in National Chemical Laboratory on contract basis
Project Code : BSC0117
Title of the Project : Plant?Microbe and Soil Interactions (PMSI)
No. of Post : 01
Qualification : M.Tech. / M.Sc. in Bioinformatics from a recognized university with minimum of 55% marks (aggregate) and sound Bioinformatics knowledge / experience
Desirable : Good knowledge of Linux (command line and GUI) and SQL; Java / Perl / Python / R / Bash programing / scripting; Analysis of NGS data; Protein modeling / docking; Development and maintenance of web & database servers, etc
Emoluments : Rs. 16,000/?
Age Limit : 28 Years
Selection Procedure : Written Test / Interview
How to apply
Applications neatly typed in the prescribed proforma (enclosed herewith) duly completed and signed together with photo?copies of relevant certificates/ testimonials and photograph should be addressed to : The Head, Biochemical Sciences Division, Attn : Dr. Narendra Kadoo, CSIR?National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune 411 008, so as to reach on or before 08/01/2016. Please superscribe the envelop “Application for Junior Project Fellow (Project: BSC0117)”.

More at http://www.ncl-india.org/files/JoinUs/JobVacancies/TemporaryJobs.aspx?

Eligibility : M Phil / Phd

Location : Thiruvananthapuram

Last Date : 30 Apr 2016

Hiring Process : Face to Face Interview
IISER -TVM

The Postdoctoral Fellowship/Research Associateship is a full-time, contractual position for highly qualified young scientists to carry out research at CCMS, IISER-TVM.

Research areas at the Centre

Quantum Chemistry/ Computational Fluid Dynamics/Condensed Matter Physics (Theory)/Genomics/Genetics/Gravitational Waves

Qualifications: PhD in Bioinformatics / Biophysics / Physics / Astrophysics / Chemistry / Mathematics / Engineering (Mechanical/Aerospace) Those who are in the final stages of their Ph.D. thesis submission are also eligible to apply. However, those candidates must have submitted the thesis at the time of the interview.

Experience: Applicants should have at least three peer reviewed publications and relevant experience in the research area they are applying for.

No. of positions: 5

Age limit: 35 yrs or below. A relaxation of 5 yrs will be applicable to candidates belonging to SC/ST/OBC and women candidates

Salary: The Fellowship carries a remuneration of INR Rs. 5,18,000 - Rs. 5,76,000 per annum (including HRA). The postdoctoral fellowship may not be held concurrently with any other national or international fellowships. It is also not transferable to any other fellowship
How to apply

Applications should reach the Head, CCMS, IISER Thiruvananthapuram,CET Campus, Engineering College PO,Thiruvananthapuram 695016 on or before April 30, 2016 by e-mail to ccms@iisertvm.ac.in by mentioning the research area name in the subject line.

More at http://www.iisertvm.ac.in/openings/read_opening/150.phpx

Lift over is the process of transferring annotations from one genome assembly to another. Usually lift over is done because there is a new, improved genome assembly for the species and good quality annotations (maybe manually curated or experimentally verified) are available on the old assembly.

The idea is simple: align the new assembly with the old one (e.g., with BLAT), process the alignment data to define how a coordinate or coordinate range on the old assembly should be transformed to the new assembly (e.g., as a chain file), transform the coordinates (e.g., with liftOver).

https://github.com/wurmlab/flo

Address of the bookmark: https://github.com/wurmlab/flo