More at http://hgdownload.cse.ucsc.edu/admin/exe/

Address of the bookmark: http://hgdownload.cse.ucsc.edu/admin/exe/

We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.

http://compbio.mit.edu/

The importance of transcriptional control has been explored in a burgeoning line of research over several decades; nevertheless, we are still far from having a complete picture of the regulatory mechanisms of genes and non-coding RNAs, and their influences on different phenotypes and disease states of a cell. Recent shifts towards large-scale analyses of transcriptional regulation on a sequence and epigenetic level are at the forefront of research, mainly due to sequencing technology advancements and a deeper understanding of the fundamental regulatory processes involved.

Arriving at a better understanding of the influence of specific parts of the overall regulatory machinery on disease states is a high priority of the group’s research agenda.

We are seeking an enthusiastic student to join the group as a PhD student. Applicants must have a BSc(Hons) or MSc degree in a relevant discipline and a willingness to learn and apply new techniques and work in a team. Both local and international students are encouraged to apply.

The studentship covers all university fees and an annual tax-exempt stipend of NZ$22,000 for three years.

Sebastian Schmeier recently joined Massey University and started his own research group in Auckland, New Zealand, a city regularly ranked one of the most livable in the world. This is your chance to experience the amazing Auckland lifestyle and the excitement of joining a young new science team, while staying connected to world class scientific networks.

To apply for the post, please send a cover letter stating your interest in the position and why you think you would be a good candidate, a Curriculum Vitae, a copy of your academic transcript, a sample of your written scientific work, and the names of three referees. Applications will be accepted until the position is filled.

Enquiries and applications to Sebastian Schmeier (s.schmeier@massey.ac.nz).

• Inchworm assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.

• Chrysalis clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.

• Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes.

More at https://github.com/trinityrnaseq/trinityrnaseq/wiki

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Download Trinity here.

Build Trinity by typing 'make' in the base installation directory.

Assemble RNA-Seq data like so:

 Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G 

Find assembled transcripts as: 'trinity_out_dir/Trinity.fasta'

Address of the bookmark: https://github.com/trinityrnaseq/trinityrnaseq/wiki

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/

Ref https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/

Address of the bookmark: https://github.com/likelet/LncPipe

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/