Showing posts with label DepthSizer. Show all posts
Showing posts with label DepthSizer. Show all posts

Thursday, 19 September 2024

SLiMSuite Release v1.12.0 (2024-09-19)


The current SLiMSuite release is v1.12.0 (2024-09-19) and can be downloaded by clicking the button (left).

In addition to the tarball available via the button (left), SLiMSuite is now available as a GitHub repository (right).

DOI

See also: Installation and Setup.


The main updates in SLiMSuite v1.12.0 are:

  • Python3. The main tools in use have been updated and checked with Python3. Some older tools might still have bugs.
  • BUSCOMP now has a new modephylofas=T to generate output of compiled and renamed files for BUSCO-based phylogenomics.
  • DepthKopy has undergone several upgrades and will now rate Duplicated BUSCOs (TRUE/FALSE) based on depth, chunk up input for multithreaded processing, and collapse features by depth to estimate total copy number. KAT can now generate the kmer usage in an alternative assembly for comparison.
  • DepthSizer has some miscellaneous bug fixes and the same parallisation added to DepthKopy.
  • Diploidocus output has been updated for ChromSyn compatibility.
  • NUMTFinder has had several coverage and depth bugs fixed.
  • PAFScaff has been updated for rapid BUSCO-based mapping. Added purechrom=T to enable reciprocal PAFScaff runs for SharpClaw
  • SynBad has received multiple updates to enable BUSCO mapping and update the assembly map output to be compatible with Telociraptor.
  • Telociraptor has received multiple updates to improve generation of tweaked assemblies from assembly maps. It also now features a chromosome sorting and renaming function based on size.
  • DepthCharge has a new minspan=INT minimum spanning bp at end of reads (trims from PAF alignments).
  • ChromSyn has undergone numerous updates and improvement: see the ChromSyn github for details.

NOTE: Several tools are now maintained and updated more regularly in their own GitHub repos.

Wednesday, 12 January 2022

SLiMSuite release v1.11.0 (2022-01-12)

DOI

SLiMSuite v1.11 sees the introduction of six genome assembly tools:

  • DepthCharge = Genome assembly quality control and misassembly repair. DepthCharge is an assembly quality control and misassembly repair program. It uses mapped long read depth of coverage to charge through a genome assembly and identify coverage “cliffs” that may indicate a misassembly. If appropriate, it will then blast the assembly into fragment at those misassemblies.
  • DepthKopy = DepthKopy: Read-depth based copy number estimation. DepthKopy applies the same single-copy read depth estimate as DepthSizer to estimate the copy number of different gene regions in a slightly modified version of the approach used in the basenji genome paper.
  • DepthSizer = DepthSizer: Read-depth based genome size prediction. DepthSizer uses long-read depth profiles and BUSCO single-copy orthologues to predict genome size. DepthSizer works on the principle that Complete BUSCO genes should represent predominantly single copy (diploid read depth) regions along with some poor quality and/or repeat regions. Assembly artefacts and collapsed repeats etc. are predicted to deviate from diploid read depth in an inconsistent manner. Therefore, even if less than half the region is actually diploid coverage, the modal read depth is expected to represent the actual single copy read depth.
  • GapSpanner = GapSpanner: Genome assembly gap long read support and reassembly tool. GapSpanner uses (or generates) a BAM file of long reads mapped to a genome assembly to assess assembly “gaps” for spanning read support. Optionally, reads spanning each gap can be extracted and re-assembled with Flye. If the new assembly spans the gap, crude gap-filling can be performed. This will be reversed if edits are not subsequently supported by spanning reads mapped onto the updated assembly.
  • NUMTFinder = NUMTFinder: Nuclear mitochondrial fragment (NUMT) search tool. NUMTFinder uses a mitochondrial genome to search against genome assembly and identify putative NUMTs. NUMT fragments are then combined into NUMT blocks based on proximity.
  • Taxolotl = Taxolotl: Genome assembly taxonomy summary and assessment tool. Taxolotl combines the MMseqs2 easy-taxonomy with GFF parsing to perform taxonomic analysis of a genome assembly (and any subsets given by taxsubsets=LIST) using an annotated proteome. Taxonomic assignments are mapped onto genes as well as assembly scaffolds and (if assembly=FILE is given) contigs.

Documentation for these tools can be found in their individual repos. Please note that individual repos may be ahead of the main SLiMSuite repo.

More information can also be found in the corresponding publications:

See also the included release_notes.txt on GitHub for a full list of the python module updates since v1.9.0.

Monday, 11 October 2021

DepthSizer v1.4.0 (IndelRatio) release

DepthSizer v1.4.0 has been released on GitHub. DepthSizer is a program to estimate genome size from an assembly, long-read sequencing data, and BUSCO single-copy orthologue predictions.

DepthSizer works on the principle that Complete BUSCO genes should represent predominantly single copy (diploid read depth) regions along with some poor quality and/or repeat regions. Assembly artefacts and collapsed repeats etc. are predicted to deviate from diploid read depth in an inconsistent manner. Therefore, even if less than half the region is actually diploid coverage, the modal read depth is expected to represent the actual single copy read depth.

This release features an extensive reworking under the hood, which moves the main calculation into R and smooths the read depth modal density calculation. Some of the older, less accurate, approaches have been dropped in favour of some additional mapping adjustments that aim to frame the upper and lower bounds of genome size.

Current citation:

Chen SH, Rossetto M, van der Merwe M, Lu-Irving P, Yap JS, Sauquet H, Bourke G, Bragg JG & Edwards RJ (preprint): Chromosome-level de novo genome assembly of Telopea speciosissima (New South Wales waratah) using long-reads, linked-reads and Hi-C. bioRxiv 2021.06.02.444084; doi: 10.1101/2021.06.02.444084.