Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence. 

Address of the bookmark: https://sourceforge.net/projects/metassembler/?source=directory

An algorithmically novel approach to construct a genome graph representation of long-read-based de novo sequence assemblies. We then provide a proof of principle by creating a genome graph of seven ethnically-diverse human genomes.

https://f1000research.com/articles/7-1391/v1

Address of the bookmark: https://github.com/NCBI-Hackathons/NovoGraph

Address of the bookmark: http://www.isical.ac.in/~bioinfo_miu/FOGSAA.htm

The MMseqs2 user guide is available as Github Wiki or as PDF file (Thanks to pandoc!)

Please cite: Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, doi: 10.1038/nbt.3988 (2017).

Address of the bookmark: https://github.com/soedinglab/MMseqs2

These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.

• Read the LSC_requirements for running LSC.
• Download and set-up the LSC package.
• Follow the tutorial to see how LSC works on some example data.
• Read the manual if anything is unclear.
• You're ready, Happy LSCing!

Latest publication

Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong 
Improving PacBio Long Read Accuracy by Short Read Alignment [Manuscript] 
PLoS ONE 2012. 7(10): e46679. doi:10.1371/journal.pone.0046679

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

Address of the bookmark: http://frodock.chaconlab.org/

We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator design that adopts modern FPGA (field-programmable gate array) architectures to further boost the performance of our algorithm.

More at https://github.com/CMU-SAFARI/Shouji

Address of the bookmark: https://github.com/CMU-SAFARI/Shouji

Address of the bookmark: https://github.com/soedinglab/MMseqs2

More at https://www.nature.com/articles/s41592-023-01970-4

Address of the bookmark: https://github.com/seryrzu/unialigner

The successful candidate will be in charge of the detection of polymorphisms including structural variants, of the comparison of multiple and diverse genomes of a same species and of the construction of pan- and core-genomes. These challenging tasks will require bioinformatics developments and implementation of methods for accommodating the high level of repetitiveness of complex genomes. The tools will be integrated into pipelines and made available to end-users through the Galaxy platform. The bioinformatician will therefore also have to provide researchers with advices on their experimental designs in order to ensure compliance of produced datasets with pipelines requirements. He/she will be hosted by a bioinformatics/informatics team (7 people) (http://moulon.inra.fr/index.php/fr/equipestransversales/atelier-de-bioinformatique) which has computational facilities and expertise in NGS data analysis, and will benefit as well from national and international collaborative networks (Aplibio http://www.renabi.fr/platforms/aplibio/, Transplant http://transplantdb.eu, AMAIZING http://www.amaizing.fr/).

The position requires a doctoral degree (PhD) in bioinformatics with strong expertise in script writing (Python/Perl) and pipeline development.

Applicants should send a CV and the names of 2 referees willing to provide a letter of recommendation to joets@moulon.inra.fr.