BOL: Related items

GenomeThreader: Gene Prediction Software

Neel — Wed, 03 Oct 2018 15:34:08 -0500

GenomeThreader is a software tool to compute gene structure predictions. The gene structure predictions are calculated using a similarity-based approach where additional cDNA/EST and/or protein sequences are used to predict gene structures via spliced alignments. GenomeThreader was motivated by disabling limitations in GeneSeqer, a popular gene prediction program which is widely used for plant genome annotation.

Address of the bookmark: http://genomethreader.org/

GLEAN: an unsupervised learning system to integrate disparate sources of gene structure evidence

Poonam Mahapatra — Sat, 02 Jun 2018 07:38:33 -0500

GLEAN is an unsupervised learning system to integrate disparate sources of gene structure evidence (gene model predictions, EST/protein genomic sequence alignments, SAGE/peptide tags, etc) to produce a consensus gene prediction, without prior training.

Address of the bookmark: https://sourceforge.net/projects/glean-gene/

miniasm: very fast OLC-based de novo assembler for noisy long reads

Jit — Mon, 27 Nov 2017 07:58:49 -0600

Miniasm is a very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final unitig sequences. Thus the per-base error rate is similar to the raw input reads.

So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are PacBio E. coli sample, ERS473430, ERS544009, ERS554120, ERS605484, ERS617393, ERS646601, ERS659581, ERS670327, ERS685285, ERS743109 and a deprecated PacBio E. coli data set. ONT data are acquired from the Loman Lab.

For a C. elegans PacBio data set (only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the HGAP3produces a 104Mb assembly with N50 1.61Mb. This dotter plot gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.

Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that minimap can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as MHAP and DALIGNER. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.

Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)

We start with an all against all comparison:

minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz

Then we can assemble

miniasm -f reads.fq reads.paf.gz > reads.gfa

Convert GFA to FASTA:

awk '/^S/{print ">"$2"\n"$3}' reads.gfa | fold > reads.fa

And then count how many contigs:

grep ">" reads.fa | wc -l

# Download sample PacBio from the PBcR website
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz | tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
# Install minimap and miniasm (requiring gcc and zlib)
git clone https://github.com/lh3/minimap && (cd minimap && make)
git clone https://github.com/lh3/miniasm && (cd miniasm && make)
# Overlap
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz
# Layout
miniasm/miniasm -f reads.fq reads.paf.gz > reads.gfa

Address of the bookmark: https://github.com/lh3/miniasm

Run miniasm assembler on nanopore reads !

Jit — Mon, 18 Dec 2017 04:07:50 -0600

Find the detail of the reads repeats:

fq2fa ONT_A.fastq ONT_A.fasta

minimap2 -xava-ont ONT_A.fasta ONT_A.fasta -t10 -X > AONT.paf

awk '{if($1==$6){print}}' AONT.paf > AONTself.paf

awk '$5=="-"' AONTself.paf | awk '{print $1}'| sort|uniq > invertedrepeat.list

Generated a few palindrome and repeats plots (highlighting only repeats largest than 10, 20 and 30 kb)

minidot -f 5 -m 30000 AONTself.paf > AONTself30000.eps
sed 's/_template_pass_FAH31515//' AONTself30000.eps > AONTself30000final.eps

minidot -f 5 -m 20000 AONTself.paf > AONTself20000.eps
sed 's/_template_pass_FAH31515//' AONTself20000.eps > AONTself20000final.eps

minidot -f 5 -m 10000 AONTself.paf > AONTself10000.eps
sed 's/_template_pass_FAH31515//' AONTself10000.eps > AONTself10000final.eps

Assemble with miniasm:

miniasm -f ONT_A.fasta AONT.paf > AONT.gfa
grep '^S' AONT.gfa |awk '{print ">"$2"\n"$3}' > AONT_miniasm.fasta

minimap2 -xasm10 AONT_miniasm.fasta AONT_miniasm.fasta -t1 -X > AONT_miniasm.paf

awk '{if($1==$6){print}}' AONT_miniasm.paf > AONT_miniasm_self.paf

minidot -f 5 -m 10000 AONT_miniasm_self.paf > AONT_miniasm_self10000.eps

Njoy the assembly !

HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads

Rahul Nayak — Tue, 08 May 2018 04:27:22 -0500

HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs [Sirén et al. 2014], we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

Circlator: automated circularization of genome assemblies using long sequencing reads

Poonam Mahapatra — Tue, 15 May 2018 09:42:32 -0500

A tool to circularize genome assemblies. The algorithm and benchmarks are described in the Genome Biology manuscript. Citation: "Circlator: automated circularization of genome assemblies using long sequencing reads", Hunt et al, Genome Biology 2015 Dec 29;16(1):294. doi: 10.1186/s13059-015-0849-0. PMID: 26714481.

Address of the bookmark: http://sanger-pathogens.github.io/circlator/

pbalign: maps PacBio reads to reference sequences and saves alignments to a BAM file

Jit — Thu, 24 May 2018 10:06:52 -0500

pbalign aligns PacBio reads to reference sequences, filters aligned reads according to user-specific filtering criteria, and converts the output to either the SAM format or PacBio Compare HDF5 (e.g., .cmp.h5) format. The output Compare HDF5 file will be compatible with Quiver if --forQuiver option is specified.

Address of the bookmark: https://github.com/PacificBiosciences/pbalign

Cerulean: A hybrid assembly using high throughput short and long reads

Rahul Nayak — Tue, 05 Jun 2018 10:10:15 -0500

Cerulean extends contigs assembled using short read datasets like Illumina paired-end reads using long reads like PacBio RS long reads. Cerulean v0.1 has been implemented with bacterial genomes in mind. The method is fully described in Deshpande, V., Fung, E. D., Pham, S., & Bafna, V. (2013). Cerulean: A hybrid assembly using high throughput short and long reads. arXiv preprint arXiv:1307.7933. http://arxiv.org/abs/1307.7933

Address of the bookmark: https://sourceforge.net/projects/ceruleanassembler/

ReMILO: reference assisted misassembly detection algorithm using short and long reads.

Jit — Fri, 06 Jul 2018 04:27:49 -0500

ReMILO, a reference assisted misassembly detection algorithm that uses both short reads and PacBio SMRT long reads. ReMILO aligns the initial short reads to both the contigs and reference genome, and then constructs a novel data structure called red-black multipositional de Bruijn graph to detect misassemblies. In addition, ReMILO also aligns the contigs to long reads and find their differences from the long reads to detect more misassemblies.

Address of the bookmark: https://github.com/songc001/remilo

Hercules: a profile HMM-based hybrid error correction algorithm for long reads

Jit — Mon, 20 Aug 2018 14:14:11 -0500

Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability:

Hercules source code is available at https://github.com/BilkentCompGen/Hercules

Address of the bookmark: https://github.com/BilkentCompGen/Hercules