Sequali

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+ Base the percentage in the overrepresented sequences section on the number
of found fragments divided by the number of sampled sequences. Previously
this was based on the number of sampled fragments, which led to very low
percentages for long read sequences, whilst also being less intuitive to
understand. There were some inconsistencies in the documentation about this
that are now fixed.
+ Add a new `meta` section to the JSON report to allow integration with
`MultiQC <https://multiqc.info>`_.
+ Add all nanopore barcode sequences and native adapters to the contaminants.
+ Add native adapters to the adapter search.

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+ Fixed an issue that caused an off by one error if start and end time
of a Nanopore run were at certain intervals.

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+ Fix bugs that were triggered when empty reads were present on
illumina and nanopore platforms.
+ Fix a bug that was triggered when a single nucleotide read was present on
a nanopore platform.
+ Add a ``--version`` command line flag.
+ Add an ``--adapter-file`` file flag which can be used to set custom adapter
files by users.

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+ Fingerprint using offsets of 64 bases from both ends of the sequence.
On nanopore sequencing this prevents taking into account adapter sequences
for the duplication estimate. It also prevents taking sequences from the
error-prone regions. The fingerprint consists of two 8 bp sequences rather
than the two 16 bp sequences that were used before. This made the fingerprint
less prone to sequencing errors, especially in long read sequencing
technologies. As a result the duplication estimate on nanopore reads
should be more accurate.
+ Added a small header with information on where to submit bug reports.
+ Use different adapter probes for nanopore adapters, such that the probes
do occur at some distance from the strand extremities. The start and end
of nanopore sequences are prone to errors and this hindered adapter
detection.
+ Distinguish between top and bottom adapters for the adapter occurrence plot.
+ Update pygal to 3.0.4 to prevent installation errors on Python 3.12.
+ Fix several divide by 0 errors that occurred on empty reads and empty files.
+ Change default fragment length from 31 to 21 which increases the sensitivity
of the overrepresented sequences module.

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+ Fixed a crash that occurred in the illumina header checking code on
illumina headers without the comment part.
+ ``--max-unique-sequences`` flag replaced with
``--overrepresentation-max-unique-fragments`` to be consistent with the
report and other flags.
+ Lots of formatting improvements were made to the report:

+ The quality distribution plot now use Matplotlib's RdBu colormap. Like
the old colormap, it goes from red to blue via white, but is much
clearer visually.
+ Tables now have zebra-style coloring and mouse-over coloring to clearly
distinguish rows.
+ The base content plot now uses a green and blue color scheme for GC and
AT bases respectively. Previously it was red and blue.
+ Sans-serif fonts used throughout the report.
+ Explanation paragraphs are now in a smaller font and italic to visually
distuingish them from data generated specifically for the sequencing
file.
+ Plots are now rendered in sans-serif rather than monospace fonts.
+ Minor formatting, spelling and style issues were fixed.
+ The programs CLI help messages have been improved by clearer phrasing,
better metavar names and consistent punctuation.
+ The reverse complement of the canonical sequence is included in the
overrepresented sequences table.
+ Make the number of threads configurable on the command line.
+ Fix build errors on windows

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+ In order to get overrepresented sequences across the entire read, reads
are cut into fragments of 31 bp which are stored and counted. If the fragment
store is full, only already stored sequences are counted. One in eight
reads is processed this way.
+ Add fingerprint-based deduplication estimation based on `a technique used in
filesystem deduplication estimation
<https://www.usenix.org/system/files/conference/atc13/atc13-xie.pdf>`_.
+ Add a BAM parser to allow reading dorado produced unaligned BAM as well as
already aligned BAM files.
+ Guess sequencing technology from the file header, so only appropriate
adapters can be loaded in the adapter searcher. This improves speed.
+ Make an assortment of nanopore adapter probes that make it possible to
distuinghish between nannopore adapters despite the nanopore adapters having
a lot of shared subsequences.
+ Add a module to retrieve nanopore specific information from the header.
+ Classify overrepresented sequences by using NCBI's UniVec database and an
assortment of nanopore adapters, ligation kits and primers.
+ Estimate duplication fractions based on counted unique sequences.
+ Add a JSON report
+ Add a progressbar powered by tqdm.
+ Implement a custom parser based on memchr for finding newlines.
+ Count overrepresented sequences using a hash table implemented in C.
+ Add a per tile sequence quality module.
+ Count adapters using a fast shift-AND algorithm.
+ Create diverse graphs using pygal based on the count matrix.
+ Implement base module using an optimised count matrix.