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Classify ChIP/ATAC-Seq peaks based on features provided in a GFF
Peaks are provided in a BED file sorted by chromosome and position. The GFF
must be sorted by chromosome and position, with gene-level features separated
by ### tags and each gene organized into subfeatures such as transcripts and
exons. This is the default for common data sources.
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Add LDFLAGS to allow RELRO
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Updates for new biolibc API
Upstream change log: https://github.com/auerlab/vcf-split/releases
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Updates for new biolibc API
Upstream change log: https://github.com/auerlab/vcf2hap/releases
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Updates for new biolibc API
Upstream change log: https://github.com/auerlab/ad2vcf/releases
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Import sam_buff_t class and VCF functions from ad2vcf
Add BED and GFF support
Isolate headers under include/biolibc
Numerous small enhancements and fixes
Upstream change log: https://github.com/auerlab/biolibc/releases
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Release notes: https://www.ncbi.nlm.nih.gov/books/NBK131777/
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change
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The distfile for minimap2 includes two different components: (i) the
minimap2 sequence mapping program itself, and (ii) a python binding
generally referred to as mappy. The initial version of this package
included only the python binding. However, it is more appropriate
that the minimap2 package should contain the program of the same name,
and a new package be created with the name mappy for the python
binding. Splitting these into two packages makes sense, because this
allows users to install the minimap2 package without python
dependencies.
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Filter reads from a FASTQ file using a list of identifiers.
Each entry in the input FASTQ file (or files) is checked against all
entries in the identifier list. Matches are included by default, or
excluded if the --invert flag is supplied. Paired-end files are kept
consistent (in order).
This is almost certainly not the most efficient way to implement this
filtering procedure. I tested a few different strategies and this one
seemed the fastest. Current timing with 16 processes is about 10
minutes per 1M paired reads with gzip'd input and output, depending on
the length of the identifier list to filter by.
usage: filter_fastq.py [-h] [-i INPUT] [-1 READ1] [-2 READ2] [-p NUM_THREADS]
[-o OUTPUT] [-f FILTER_FILE] [-v] [--gzip]
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Introduction
Beagle is a software package for phasing genotypes and for imputing
ungenotyped markers. Beagle version 5.2 provides significantly faster
genotype phasing than version 5.1
Citation
If you use Beagle in a published analysis, please report the program
version and cite the appropriate article.
The Beagle 5.2 genotype imputation method is described in:
B L Browning, Y Zhou, and S R Browning (2018). A one-penny imputed
genome from next generation reference panels. Am J Hum Genet
103(3):338-348. doi:10.1016/j.ajhg.2018.07.015
The most recent reference for Beagle's phasing method is:
S R Browning and B L Browning (2007) Rapid and accurate haplotype
phasing and missing data inference for whole genome association
studies by use of localized haplotype clustering. Am J Hum Genet
81:1084-1097. doi:10.1086/521987
This reference will be updated when the Beagle version 5 phasing
method is published.
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## Description
Racon is intended as a standalone consensus module to correct raw
contigs generated by rapid assembly methods which do not include a
consensus step. The goal of Racon is to generate genomic consensus
which is of similar or better quality compared to the output generated
by assembly methods which employ both error correction and consensus
steps, while providing a speedup of several times compared to those
methods. It supports data produced by both Pacific Biosciences and
Oxford Nanopore Technologies.
Racon can be used as a polishing tool after the assembly with **either
Illumina data or data produced by third generation of
sequencing**. The type of data inputed is automatically detected.
Racon takes as input only three files: contigs in FASTA/FASTQ format,
reads in FASTA/FASTQ format and overlaps/alignments between the reads
and the contigs in MHAP/PAF/SAM format. Output is a set of polished
contigs in FASTA format printed to stdout. All input files **can be
compressed with gzip** (which will have impact on parsing time).
Racon can also be used as a read error-correction tool. In this
scenario, the MHAP/PAF/SAM file needs to contain pairwise overlaps
between reads **including dual overlaps**.
A **wrapper script** is also available to enable easier usage to the
end-user for large datasets. It has the same interface as racon but
adds two additional features from the outside. Sequences can be
**subsampled** to decrease the total execution time (accuracy might be
lower) while target sequences can be **split** into smaller chunks and
run sequentially to decrease memory consumption. Both features can be
run at the same time as well.
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## Users' Guide
Minimap2 is a versatile sequence alignment program that aligns DNA or
mRNA sequences against a large reference database. Typical use cases
include: (1) mapping PacBio or Oxford Nanopore genomic reads to the
human genome; (2) finding overlaps between long reads with error rate
up to ~15%; (3) splice-aware alignment of PacBio Iso-Seq or Nanopore
cDNA or Direct RNA reads against a reference genome; (4) aligning
Illumina single- or paired-end reads; (5) assembly-to-assembly
alignment; (6) full-genome alignment between two closely related
species with divergence below ~15%.
For ~10kb noisy reads sequences, minimap2 is tens of times faster than
mainstream long-read mappers such as BLASR, BWA-MEM, NGMLR and
GMAP. It is more accurate on simulated long reads and produces
biologically meaningful alignment ready for downstream analyses. For
>100bp Illumina short reads, minimap2 is three times as fast as
BWA-MEM and Bowtie2, and as accurate on simulated data. Detailed
evaluations are available from the minimap2 paper or the preprint.
Release 2.18-r1015 (9 April 2021)
---------------------------------
This release fixes multiple rare bugs in minimap2 and adds additional
functionality to paftools.js.
Changes to minimap2:
* Bugfix: a rare segfault caused by an off-by-one error (#489)
* Bugfix: minimap2 segfaulted due to an uninitilized variable (#622 and #625).
* Bugfix: minimap2 parsed spaces as field separators in BED (#721). This led
to issues when the BED name column contains spaces.
* Bugfix: minimap2 `--split-prefix` did not work with long reference names
(#394).
* Bugfix: option `--junc-bonus` didn't work (#513)
* Bugfix: minimap2 didn't return 1 on I/O errors (#532)
* Bugfix: the `de:f` tag (sequence divergence) could be negative if there were
ambiguous bases
* Bugfix: fixed two undefined behaviors caused by calling memcpy() on
zero-length blocks (#443)
* Bugfix: there were duplicated SAM @SQ lines if option `--split-prefix` is in
use (#400 and #527)
* Bugfix: option -K had to be smaller than 2 billion (#491). This was caused
by a 32-bit integer overflow.
* Improvement: optionally compile against SIMDe (#597). Minimap2 should work
with IBM POWER CPUs, though this has not been tested. To compile with SIMDe,
please use `make -f Makefile.simde`.
* Improvement: more informative error message for I/O errors (#454) and for
FASTQ parsing errors (#510)
* Improvement: abort given malformatted RG line (#541)
* Improvement: better formula to estimate the `dv:f` tag (approximate sequence
divergence). See DOI:10.1101/2021.01.15.426881.
* New feature: added the `--mask-len` option to fine control the removal of
redundant hits (#659). The default behavior is unchanged.
Changes to mappy:
* Bugfix: mappy caused segmentation fault if the reference index is not
present (#413).
* Bugfix: fixed a memory leak via 238b6bb3
* Change: always require Cython to compile the mappy module (#723). Older
mappy packages at PyPI bundled the C source code generated by Cython such
that end users did not need to install Cython to compile mappy. However, as
Python 3.9 is breaking backward compatibility, older mappy does not work
with Python 3.9 anymore. We have to add this Cython dependency as a
workaround.
Changes to paftools.js:
* Bugfix: the "part10-" line from asmgene was wrong (#581)
* Improvement: compatibility with GTF files from GenBank (#422)
* New feature: asmgene also checks missing multi-copy genes
* New feature: added the misjoin command to evaluate large-scale misjoins and
megabase-long inversions.
Although given the many bug fixes and minor improvements, the core algorithm
stays the same. This version of minimap2 produces nearly identical alignments
to v2.17 except very rare corner cases.
Now unimap is recommended over minimap2 for aligning long contigs against a
reference genome. It often takes less wall-clock time and is much more
sensitive to long insertions and deletions.
(2.18: 9 April 2021, r1015)
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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][PB-151103], [ERS473430][ERS473430], [ERS544009][ERS544009],
[ERS554120][ERS554120], [ERS605484][ERS605484],
[ERS617393][ERS617393], [ERS646601][ERS646601],
[ERS659581][ERS659581], [ERS670327][ERS670327],
[ERS685285][ERS685285], [ERS743109][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 HGAP3 produces 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.
## Algorithm Overview
1. Crude read selection. For each read, find the longest contiguous region
covered by three good mappings. Get an approximate estimate of read
coverage.
2. Fine read selection. Use the coverage information to find the good regions
again but with more stringent thresholds. Discard contained reads.
3. Generate a string graph. Prune tips, drop weak overlaps and
collapse short bubbles. These procedures are similar to those
implemented in short-read assemblers.
4. Merge unambiguous overlaps to produce unitig sequences.
## Limitations
1. Consensus base quality is similar to input reads (may be fixed with a
consensus tool).
2. Only tested on a dozen of high-coverage PacBio/ONT data sets (more testing
needed).
3. Prone to collapse repeats or segmental duplications longer than input reads
(hard to fix without error correction).
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v0.5.1
Add py.typed and distribute .pyi files
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most of these simply extend matching from "aarch64" to "aarch64eb"
in various forms of code. most remaining uses in pkgsrc of
"MACHINE_ARCH == aarch64" are because of missing aarch64eb support,
such as most of the binary-bootstrap requiring languages like rust,
go, and java.
no pkg-bump because this shouldn't change packages on systems that
could already build all of these.
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-This is just a small release to fix some issues with the (possibly) renamed
*.so/*.dll files after removing Qt5 support. In case you were using Molsketch
prior to version 0.7.1, it will ask you to update the corresponding settings at
start up.
For Windows users, there will be an online installer, as in version 0.7.1, but
this will now reside in a separate folder and not be updated as frequently as
Molsketch itself. Updates will instead be made available in the online
repository at github from which the installer will fetch them. Just start the
installer and select the update option
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v3.4 (2021-03-30)
-----------------
* :issue:`481`: An experimental single-file Windows executable of Cutadapt
is `available for download on the GitHub "releases"
page <https://github.com/marcelm/cutadapt/releases>`_.
* :issue:`517`: Report correct sequence in info file if read was reverse complemented
* :issue:`517`: Added a column to the info file that shows whether the read was
reverse-complemented (if ``--revcomp`` was used)
* :issue:`320`: Fix (again) "Too many open files" when demultiplexing
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vcf2hap is a simple tool for generating a .hap file from a VCF. The .hap file
is required by haplohseq.
vcf2hap is extremely fast and requires a trivial amount of memory regardless of
the size of the VCF file.
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ad2vdf extracts allelic depth info from a SAM stream and adds it to a
corresponding single-sample VCF file.
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Vcf-split splits a multi-sample VCF into single-sample VCFs, writing thousands
of output files simultaneously. Parsing the TOPMed human chromosome 1 BCF
with bcftools takes two days, so extracting the 137,977 samples one at a time
or using thousands of parallel readers of the same file is impractical.
Vcf-split solves this by generating thousands of single-sample outputs during
a single sweep through the multi-sample input.
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Biolibc is a library of fast, memory-efficient, low-level functions for
processing biological data. Like libc, it consists of numerous disparate,
general-purpose functions which could be used by a wide variety of
applications. These include functions for streaming common file formats such
as SAM and VCF, string functions specific to bioinformatics, etc.
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Generate random genomic data in FASTA/FASTQ, SAM, or VCF format, suitable for
small academic examples or test inputs of arbitrary size. Output can be piped
directly to programs or redirected to a file and edited to taste.
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biology/bcftools: Update to 1.12
biology/samtools: Update to 1.12
Numerous enhancements, performance improvements, and bug fixes since 1.10
Minimized pkgsrc patches in all three packages
Moved htslib to custom tarball since Github-generated distfiles are incomplete
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The dependency is visible for packages using the LBFGS solver.
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Unfortunately, there were quite some unintended bugs in the last version (some
of them older than that, however), which are being addressed by this version.
Saving files and re-opening might have sometimes led to crashes due to
inconsistencies in the drawing's data. This should now be fixed in, if not all
at least most of the cases.
Likewise, copying, cutting, and pasting is more robust now.
The last version prematurely updated some code leading to incompatibilities
with older versions of Qt (especially pre-5.14). These older versions should
now work again; support for Qt 4, on the other hand is completely removed, as
it is doubtful whether that still worked anyway.
Translations should now really work throughout Molsketch (currently supported
languages: Chinese, English, German, Greek).
Finally, for Windows, an installer is provided, which will download from a
repository hosted at github.
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v3.3:
* :issue:`504`: Fix a crash on Windows.
* :issue:`490`: When ``--rename`` is used with ``--revcomp``, disable adding the
``rc`` suffix to reads that were reverse-complemented.
* Also, there is now a ``{rc}` template variable for the ``--rename`` option, which
is replaced with "rc" if the read was reverse-complemented (and the empty string if not).
* :issue:`512`: Fix issue :issue:`128` once more (the “Reads written” figure in the report
incorrectly included both trimmed and untrimmed reads if ``--untrimmed-output`` was used).
* :issue:`515`: The report is now send to stderr if any output file is
written to stdout
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The Integrative Genomics Viewer (IGV) is a high-performance visualization tool
for interactive exploration of large, integrated genomic datasets. It supports
a wide variety of data types, including array-based and next-generation
sequence data, and genomic annotations.
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