BOL: Related items

CANU genome assembly parameters !

Rahul Nayak — Mon, 07 Jan 2019 08:40:37 -0600

Choose the appropriate parameters to run Canu and run it. The assembly will take about an hour. You can use two cores (parameter -maxThreads=2) and you would like to disable cluster option, since we compute on a single Amazon server set off the option to compute on cluster useGrid=false. This specifications should be for your project discussed with a local computing guru. The parameters that are in square brackets [] are optional, symbol | stands for "or".

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

Evaluation of genome assembly software based on long reads

BioStar — Fri, 01 Feb 2019 11:55:54 -0600

TGS technologies have been used to produce highly accurate de novo assemblies of hundreds of microbial genomes and highly contiguous reconstructions of many dozens of plant and animal genomes, enabling new insights into evolution and sequence diversity. They have also been applied to resequencing analyses, to create detailed maps of structural variations in many species. Also, these new technologies have been used to fill in many of the gaps in the human reference genome.

In this report, we compare and evaluate several genome assembly software based on TSG technology. The experimentation has been performed on 4 reference genomes and the results evaluated with the QUAST software. The 11 software that have been evaluated are: Celera Assembler , Falcon , Miniasm, Newbler , SGA Assembler, Smartdenovo, Abruijn, Ra, DBG2OLC, Spades and Cerulean. The first 8 software use only long reads, while the 3 last software can merge long and short reads

Apollo: First instantaneous, collaborative genomic annotation editor available on the Web

Jit — Fri, 31 May 2019 19:55:39 -0500

Apollo is a plug-in for the JBrowse Genome Viewer.
In addition to genes and pseudogenes, users can annotate ncRNAs (snRNA, snoRNA, tRNA, rRNA), miRNAs, repeat regions, and transposable elements; each annotation type has its own configuration of the ‘Information Editor’.
History tracking with undo/redo functions is available.
Users are able to directly set an annotation to a specific state, choosing from the ‘History’ display.
Adding and updating PubMed IDs will prompt users with a publication title to confirm their submission.
Gene Ontology (GO) terms are supported and GO ID auto-completion has been incorporated.
Users may access a ‘Recent Changes’ page.
Help page with Apollo specific content is available.

Address of the bookmark: http://genomearchitect.github.io/

simuG: a general-purpose genome simulator

BioStar — Thu, 28 Nov 2019 04:33:18 -0600

Simulated genomes with pre-defined and random genomic variants can be very useful for benchmarking genomic and bioinformatics analyses. Here we introduce simuG, a lightweight tool for simulating the full-spectrum of genomic variants (single nucleotide polymorphisms, Insertions/Deletions, copy number variants, inversions and translocations) for any organisms (including human). The simplicity and versatility of simuG make it a unique general-purpose genome simulator for a wide-range of simulation-based applications.

Address of the bookmark: https://github.com/yjx1217/simuG

MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization

Jit — Fri, 24 Jan 2020 04:09:15 -0600

MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from https://github.com/linzhi2013/MitoZ.

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

Liu Lab

Tue, 04 Feb 2020 06:27:02 -0600

Shirley is a computational biologist with expertise in cancer epigenetics. Her research focuses on algorithm development and integrative mining from big data generated on microarrays, massively parallel sequencing, and other high throughput techniques to model the specificity and function of transcription factors, chromatin regulators and lncRNAs in tumor development, progression, drug response and resistance.

https://liulab-dfci.github.io/software/

shinyChromosome:a GUI for the interactive creation of non-circular whole genome diagrams

Jit — Sat, 29 Feb 2020 00:39:50 -0600

shinyChromosome is a graphical user interface for interactive creation of non-circular whole genome diagrams developed using the R Shiny package.

To create single-genome plot by aligning genome data along all chromosomes of a single genome, go to the Single-genome plot menu.

To cretae two-genome plot for comparison of data across two genomes, go to the Two-genome plot menu.

For the detail format of input data, check the Input data format submenu of the Help menu.

shinyChromosome is deployed at http://150.109.59.144:3838/shinyChromosome/, http://shinyChromosome.ncpgr.cn, and https://yimingyu.shinyapps.io/shinyChromosome for online use. The source code and manual of shinyChromosome are freely available at https://github.com/venyao/shinyChromosome.

https://yimingyu.shinyapps.io/shinychromosome/

https://www.sciencedirect.com/science/article/pii/S1672022919301883

Address of the bookmark: https://yimingyu.shinyapps.io/shinychromosome/

RefKA: A fast and efficient long-read genome assembly approach for large and complex genomes

Rahul Nayak — Fri, 01 May 2020 03:00:40 -0500

RefKA, a reference-based approach for long read genome assembly. This approach relies on breaking up a closely related reference genome into bins, aligning k-mers unique to each bin with PacBio reads, and then assembling each bin in parallel followed by a final bin-stitching step.

Address of the bookmark: https://github.com/AppliedBioinformatics/RefKA

merqury: Evaluate genome assemblies with k-mers

Jit — Fri, 03 Jul 2020 19:29:34 -0500

Often, genome assembly projects have illumina whole genome sequencing reads available for the assembled individual. The k-mer spectrum of this read set can be used for independently evaluating assembly quality without the need of a high quality reference. Merqury provides a set of tools for this purpose.

More at https://www.biorxiv.org/content/10.1101/2020.03.15.992941v1.full

Address of the bookmark: https://github.com/marbl/merqury

HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding.

Jit — Mon, 26 Oct 2020 21:23:36 -0500

Despite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding.

More at https://github.com/esolares/HapSolo

Address of the bookmark: https://github.com/esolares/HapSolo