ABySS Explorer

Interactive Java application that uses a novel graph-based representation to display a sequence assembly and associated metadata

http://www.bcgsc.ca/platform/bioinfo/software/abyss-explorer

BamView

Genome browser and annotation tool that allows visualization of sequence features, next-generation sequencing (NGS) data and the results of analyses within the context of the sequence, and also its six-frame translation

http://www.sanger.ac.uk/resources/software/artemis/

DNannotator 

Annotation web toolkit for regional genomic sequences

http://bioapp.psych.uic.edu/DNannotator.htm

JVM 

Java Visual Mapping tool for NGS reads

http://www.springer.com/cda/content/document/cda_downloaddocument/9789401792448-c2.pdf?SGWID=0-0-45-1487072-p176815501

LookSeq 

Web-based visualization of sequences derived from multiple sequencing technologies. Low- or high-depth read pileups and easy visualization of putative single nucleotide and structural variation

http://lookseq.sourceforge.net

MagicViewer 

Visualization of short read alignment, identification of genetic variation and association with annotation information of a reference genome

http://bioinformatics.zj.cn/magicviewer/

MapView 

Alignments of huge-scale single-end and pair-end short reads

http://omictools.com/mapview-s1367.html

MultiPipMaker

Computes alignments of similar regions in two DNA sequences. The resulting alignments are summarized with a ‘percent identity plot’ (pip)

http://pipmaker.bx.psu.edu/pipmaker/

PileLineGUI 

Handling genome position files in NGS studies

http://sing.ei.uvigo.es/pileline/pilelinegui.html

SAMtools tview 

Simple and fast text alignment viewer; NGS compatible

http://www.htslib.org/

SEWAL

Uses a locality-sensitive hashing algorithm to enumerate all unique sequences in an entire Illumina sequencing run

http://www.sourceforge.net/projects/sewal

STAR 

A web-based integrated solution to management and visualization of sequencing data

http://wanglab.ucsd.edu/star/browser

SVA 

Software for annotating and visualizing sequenced human genomes

http://www.svaproject.org

Viewer (IGV) 

Visualization of large heterogeneous datasets, providing a smooth and intuitive user experience at all levels of genome resolution

https://www.broadinstitute.org/igv/

ZOOM Lite 

NGS data mapping and visualization software

http://bioinfor.com/zoom/lite/

## Package Plan ##

environment location: /home/urbe/anaconda3

added / updated specs:
- salmon

The following packages will be downloaded:

package | build
---------------------------|-----------------
boost-1.64.0 | py36_4 331 KB conda-forge
jemalloc-5.1.0 | hfc679d8_0 8.2 MB conda-forge
boost-cpp-1.64.0 | 1 17.8 MB conda-forge
salmon-0.10.2 | 1 3.7 MB bioconda
conda-4.5.5 | py36_0 624 KB conda-forge
tbb-2018_20171205 | 0 1.2 MB conda-forge
------------------------------------------------------------
Total: 31.8 MB

The following NEW packages will be INSTALLED:

boost: 1.64.0-py36_4 conda-forge
boost-cpp: 1.64.0-1 conda-forge
jemalloc: 5.1.0-hfc679d8_0 conda-forge
salmon: 0.10.2-1 bioconda
tbb: 2018_20171205-0 conda-forge

The following packages will be UPDATED:

conda: 4.5.4-py36_0 conda-forge --> 4.5.5-py36_0 conda-forge

Proceed ([y]/n)? y

Downloading and Extracting Packages
boost-1.64.0 | 331 KB | ####################################################################################################################################### | 100%
jemalloc-5.1.0 | 8.2 MB | ####################################################################################################################################### | 100%
boost-cpp-1.64.0 | 17.8 MB | ####################################################################################################################################### | 100%
salmon-0.10.2 | 3.7 MB | ####################################################################################################################################### | 100%
conda-4.5.5 | 624 KB | ####################################################################################################################################### | 100%
tbb-2018_20171205 | 1.2 MB | ####################################################################################################################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

Address of the bookmark: https://github.com/clwgg/nQuireunder

usage:   canu [-correct | -trim | -assemble | -trim-assemble] \
              [-s ] \
               -p  \
               -d  \
               genomeSize=[g|m|k] \
               -maxThreads=2 \
               useGrid=false \
              [other-options] \
               read_file.fastq.gz

A default Canu run produces usually high quality assembly, example of a command that was used for testing can be found below. However, there are still a lot of parameters that are possible to tweak. For example if we desire to assemble haplotypes separately of if we want to smash them together, we can alternate the error correction process.

canu -p test_asmbl \
     -d asm_test3 \
     genomeSize=2m \
     -maxThreads=2 useGrid=false \
     -pacbio-raw \ ~/pacbio/dna/sample_reads.fastq.gz

There is a brilliant section in documentation about parameter tweaking.

The output directory contains will contain many files. The most interesting ones are:

• *.correctedReads.fasta.gz : file containing the input sequences after correction, trim and split based on consensus evidence.
• *.trimmedReads.fastq : file containing the sequences after correction and final trimming
• *.layout : file containing informations about read inclusion in the final assembly
• *.gfa : file containing the assembly graph by Canu
• *.contigs.fasta : file containing everything that could be assembled and is part of the primary assembly

The basic stats of assembly can be read from reports generated by the assembler, or calculated using standard UNIX command line tools.

More at https://canu.readthedocs.io/en/latest/faq.html

Introduction to bioinformatics is a course for biologists and clinicians that would like to learn more about the way bioinformatics is used in healthcare, biotech and pharmaceuitcal industry as well as basic research. The course covers many of the topics transformed by the emergence of big data and computational technologies. To learn more about the course, visit: https://edu.t-bio.info/course/introduction-bioinformatics/

Workshops are conducted in regular intervals on Drug Designing, Protein Modeling and Simulation, Chemoinformatics, Bioinformatics etc.The workshops are highly beneficial for working professionals, students, researcher for enhancements of the skills in short duration.

Address of the bookmark: https://github.com/imgag/ClinCNV

Additional Information:

If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).

Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).

Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.

Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.

More at https://genohub.com/ngs-instrument-guide/

Address of the bookmark: https://genohub.com/ngs-instrument-guide/

A recent study published in Genome Biology (https://link.springer.com/article/10.1186/s13059-026-04280-y) addressed this challenge by introducing dicast (https://github.com/burgshrimps/dicast), a machine-learning approach for detecting structural variants in short-read sequencing data. Structural variants, such as large deletions, insertions, duplications, and inversions, can have significant biological and clinical implications, yet they are challenging to identify with short-read technologies. Because different detection methods frequently yield divergent results, researchers commonly employ consensus calling, considering a variant valid only if multiple tools detect it. While this approach reduces false positives, it relies on the potentially flawed assumption that the majority is always correct.

The researchers explored the impact of evaluating the supporting evidence for each variant, rather than simply tallying the number of algorithms that identified it. To establish a ground truth, they analyzed nine genomes using multiple sequencing technologies and 15 detection methods, initially identifying approximately 35 million potential variants. Through extensive filtering, evidence integration, and manual review of over 11,500 variants, they developed a robust benchmark comprising more than 236,000 structural variants. The findings underscored the complexity of the problem: short-read methods detected fewer than half of deletions and less than 10 percent of insertions, with performance declining markedly in repetitive genomic regions. In contrast, long-read technologies demonstrated superior detection capabilities. However, replacing the substantial volume of existing short-read data in clinical and research settings is not immediately feasible. Consequently, the researchers questioned whether short-read data might harbor more information than conventional analytical pipelines currently extract.

This line of inquiry led to the development of dicast. Rather than merely confirming agreement among multiple tools, dicast identifies patterns in sequencing data, including split and clipped reads, discordant read pairs, alignment characteristics, and the surrounding genomic context. An XGBoost machine-learning model evaluates which combinations of these signals are indicative of genuine structural variants. Thus, the approach shifts from tallying algorithmic consensus to interpreting the underlying evidence.

The researchers subsequently conducted a targeted evaluation by examining structural variants detected by only a single short-read tool, which are typically missed by consensus-based approaches. dicast successfully recovered approximately 81% of these single-caller deletions, insertions, and duplications. The signals for these variants were present in the data, but conventional filtering methods failed to integrate them effectively.

The utility of dicast was further demonstrated in cohorts with rare diseases, including congenital limb malformations, atrial fibrillation, and neuromuscular disorders. In one instance, dicast achieved a deletion recall rate of approximately 0.96, compared to 0.74 using consensus calling. The median number of variants requiring manual review was 29 per sample. Among 31 experimentally validated variants that standard filters would have missed, dicast identified 12, whereas consensus calling detected only one.

Overall, dicast identified approximately 20 percent more potential disease-causing deletions than consensus-based methods. While a 20 percent increase may appear modest, in clinical genomics such improvements can have significant practical implications. Missing a deletion may leave a case unresolved, whereas detecting a structural variant can provide critical diagnostic insights.

The study does not claim that machine learning has rendered short-read sequencing superior to long-read approaches. Instead, the results underscore the effectiveness of long-read sequencing for structural variant detection. However, dicast highlights a more nuanced perspective: substantial biological information may still be recoverable from the extensive short-read datasets already available.

The principal lesson extends beyond the detection of structural variants. For many years, bioinformatics pipelines have relied on threshold-based criteria, such as minimum coverage, quality scores, or support from multiple tools. While these rules are useful, biological phenomena do not always conform to rigid checklists; multiple weak signals, when considered collectively, can provide compelling evidence.

This perspective prompts consideration of the solitary variant: one algorithm identifies it, while several others do not. Traditional consensus techniques might have dismissed it, yet machine learning approaches evaluate the available evidence to determine whether the variant is plausible.

Occasionally, the most significant variant within a genome is the one that is almost universally overlooked.

Read more at https://link.springer.com/article/10.1186/s13059-026-04280-y