BOL: Related items

Minia

Jit — Thu, 08 Dec 2016 05:07:00 -0600

Minia is a short-read assembler based on a de Bruijn graph, capable of assembling a human genome on a desktop computer in a day. The output of Minia is a set of contigs. Minia produces results of similar contiguity and accuracy to other de Bruijn assemblers (e.g. Velvet).

Download

Minia 2.0.7 Linux 64-bits binaries (Source code) (Legacy codebase)

For the impatient

A typical Minia command line looks like:

./minia -in reads.fa -kmer-size 31 -abundance-min 3 -out output_prefix

Type

./minia

for a quick explanation of the parameters.

For more information, refer to the manual.

KmerGenie can be used to determine the best k-mer size, minimum abundance of correct k-mers, and genome size estimation for your dataset.

Address of the bookmark: http://minia.genouest.org/

Cutadapt

Bulbul — Wed, 14 Dec 2016 09:59:52 -0600

Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.

Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.

If you use cutadapt, please cite DOI:10.14806/ej.17.1.200 .

More at https://github.com/marcelm/cutadapt

Address of the bookmark: http://cutadapt.readthedocs.io/en/stable/guide.html

GARM:Genome Assembly, Reconciliation and Merging

Jit — Mon, 19 Dec 2016 06:03:02 -0600

The pipeline is based mainly implemented using Perl scripts and modules and third-party open source software like the AMOS (Myers et al., 2000) and MUMmer (Kurtz et al., 2004) packages. The pipeline was tested on Debian, Ubuntu, Fedora and BioLinux distributions. The method merges contigs or scaffolds from different assemblers using the same or different sequencing technologies. When scaffolds are provided, a process of finding probable compressions or extensions (CE) problems in the assemblies can be per-formed; contigs are joined back into scaffolds after gap recalculation

Address of the bookmark: http://garm-meta-assem.sourceforge.net/

quickmerge: A simple and fast metassembler and assembly gap filler designed for long molecule based assemblies.

Jit — Mon, 19 Dec 2016 10:23:36 -0600

quickmerge uses a simple concept to improve contiguity of genome assemblies based on long molecule sequences, often with dramatic outcomes. The program uses information from assemblies made with illumina short reads and PacBio long reads to improve contiguities of an assembly generated with PacBio long reads alone. This is counterintuitive because illumina short reads are not typically considered to cover genomic regions which PacBio long reads cannot. Although we have not evaluated this program for assemblies generated with Oxford nanopore sequences, the program should work with ONP-assemblies too.

Address of the bookmark: https://github.com/mahulchak/quickmerge

BUSCO

Jitendra Narayan — Sun, 07 Feb 2016 16:02:39 -0600

Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs

More at http://busco.ezlab.org/

Address of the bookmark: http://busco.ezlab.org/

Pilon

Rahul Nayak — Mon, 08 Feb 2016 15:56:18 -0600

Pilon is a software tool which can be used to:

Automatically improve draft assemblies
Find variation among strains, including large event detection

Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:

Single base differences
Small indels
Larger indel or block substitution events
Gap filling
Identification of local misassemblies, including optional opening of new gaps

More at https://github.com/broadinstitute/pilon/wiki

Address of the bookmark: https://github.com/broadinstitute/pilon/wiki

Understanding Fastqc Output

Jit — Fri, 15 Apr 2016 05:47:40 -0500

Understanding Following table and graphs

Duplication level
kmer profile
per base GC content
per base N content
per base quality
per base sequence content
per sequence GC content
per sequence quality
sequence length distribution

More at http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/

Address of the bookmark: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/

ALE: a Generic Assembly Likelihood Evaluation Framework for Assessing the Accuracy of Genome and Metagenome Assemblies

Neel — Tue, 26 Apr 2016 03:38:43 -0500

Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.

More at http://www.ncbi.nlm.nih.gov/pubmed/23303509

Address of the bookmark: http://sc932.github.io/ALE/about.html

RECORD

Bulbul — Fri, 25 Nov 2016 08:23:36 -0600

Background. Next-generation sequencing technologies are now producing multiple times the genome size in total reads from a single experiment. This is enough information to reconstruct at least some of the differences between the individual genome studied in the experiment and the reference genome of the species. However, in most typical protocols, this information is disregarded and the reference genome is used. Results. We provide a new approach that allows researchers to reconstruct genomes very closely related to the reference genome (e.g., mutants of the same species) directly from the reads used in the experiment. Our approach applies de novo assembly software to experimental reads and so-called pseudoreads and uses the resulting contigs to generate a modified reference sequence. In this way, it can very quickly, and at no additional sequencing cost, generate new, modified reference sequence that is closer to the actual sequenced genome and has a full coverage. In this paper, we describe our approach and test its implementation called RECORD. We evaluate RECORD on both simulated and real data. We made our software publicly available on sourceforge. Conclusion. Our tests show that on closely related sequences RECORD outperforms more general assisted-assembly software.

More at https://sourceforge.net/projects/record-genome-assembler/files/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pubmed/26558255

SGA: String Graph Assembler

Jit — Thu, 08 Dec 2016 05:08:59 -0600

SGA is a de novo genome assembler based on the concept of string graphs. The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.

More at

https://github.com/jts/sga

SGA dependencies:
-google sparse hash library (http://code.google.com/p/google-sparsehash/)
-the bamtools library (https://github.com/pezmaster31/bamtools)
-zlib (http://www.zlib.net/)
-(optional but suggested) the jemalloc memory allocator (http://www.canonware.com/jemalloc/download.html)

Address of the bookmark: https://github.com/jts/sga