lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.

Address of the bookmark: https://github.com/vpc-ccg/lordfast

Address of the bookmark: https://github.com/dglemay/G-NEST

Key Features of BLAST:
1. Sequence Comparison: BLAST searches for local alignments between the query sequence and sequences in a database. It identifies regions of similarity, which can help infer functional and evolutionary relationships.

2. Speed and Efficiency: BLAST uses heuristic algorithms, making it faster than exhaustive search methods, suitable for large-scale database searches.

3. Versatility: There are several versions of BLAST for different types of sequence comparisons:
- blastn: Compares a nucleotide query sequence against a nucleotide sequence database.
- blastp: Compares a protein query sequence against a protein sequence database.
- blastx: Compares a nucleotide query sequence translated in all reading frames against a protein sequence database.
- tblastn: Compares a protein query sequence against a nucleotide sequence database translated in all reading frames.
- tblastx: Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

4. Scoring and E-value: BLAST results are scored based on the quality and length of the alignments. The E-value (expect value) indicates the number of alignments one can expect to find by chance, with lower E-values representing more significant matches.

5. Output Formats: BLAST provides results in various formats, including plain text, HTML, XML, and JSON, making it adaptable for different types of analyses and integrations with other tools.

Applications of BLAST:
- Genomic Research: Identifying genes, understanding genetic diversity, and mapping genome sequences.
- Protein Function Prediction: Inferring the function of unknown proteins by comparing them to known protein sequences.
- Evolutionary Studies: Exploring evolutionary relationships between organisms by comparing their genetic material.
- Medical Research: Identifying pathogens, understanding disease mechanisms, and developing treatments by comparing sequences of interest.

Overall, BLAST is an essential tool in bioinformatics, offering a reliable and efficient way to analyze and interpret biological sequence data.

Address of the bookmark: http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html

Relying on unique software architecture, PLAST takes full advantage of recent multi-core personal computers without requiring any additional hardware devices.

PLAST stands for Parallel Local Sequence Alignment Search Tool and is was published in BMC Bioinformatics.

PLAST is a general purpose sequence comparison tool providing the following benefits:

• PLAST is a high-performance sequence comparison tool designed to compare two sets of sequences (query vs. reference),
• Reduces the processing time of sequences comparisons while providing highest quality results,
• Contains a fully integrated data filtering engine capable of selecting relevant hits with user-defined criteria (E-Value, identity, coverage, alignment length, etc.),
• Does not require any additional hardware, since it is a software solution. It is easy to install, cost-effective, takes full advantage of multi-core processors and uses a small RAM footprint,
• Ready to be used on desktop computer, cluster, cloud as well as within distributed system running Hadoop.

https://plast.inria.fr/

Address of the bookmark: https://plast.inria.fr/

Main features:

1. works with any number of scaffold assemblies in de-novo non-progressive fashion
2. allows to simultaneously work with scaffold assemblies obtained from any in silico and in vitro techniques, supporting multiple existing formats via built-in converters
3. creates an extensive report with several comparative quality metrics (both on assembly level and on the level of individual assembly points)
4. constructs a merged combined scaffold assembly
5. provides an interactive framework for a visual comparative analysis of the given assemblies

Address of the bookmark: https://cblab.org/camsa/

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/

python finisherSC.py destinedFolder mummerPath

If you are running on server computer and would like to use multiple threads, then the following commands can generate 20 threads to run FinisherSC.

python finisherSC.py -par 20 destinedFolder mummerPath

Sometimes, if the names of raw reads and contigs consists of special characters/formats, FinisherSC/MUMmer may not parse them correctly. In that case, you want to have a quick renaming of the names of contigs/reads in contigs.fasta or raw_reads.fasta using the following command.

    perl -pe 's/>[^\$]*$/">Seg" . ++$n ."\n"/ge' raw_reads.fasta > newRaw_reads.fasta
    cp newRaw_reads.fasta raw_reads.fasta
    perl -pe 's/>[^\$]*$/">Seg" . ++$n ."\n"/ge' contigs.fasta > newContigs.fasta
    cp newContigs.fasta contigs.fasta

Address of the bookmark: https://github.com/kakitone/finishingTool