From high-throughput sequencing mapped reads (SAM/BAM), GeneAbacus:

• Creates profiles representing coverage depth per nucleotide,
• Counts reads mapped within user selected features such as chromosomes or genes.

Features

• Input
  • mRNA-seq, Ribo-seq, ChIP-seq, CLIP-seq, Structure-seq, Massively Parallel Reporter Assays (MPRAs) etc
  • Any type of features, e.g. chromosomes, genes, mRNAs or constructs from MPRAs using the FON or tab format.
  • Unsorted or sorted SAM/BAM files. SAM files can be compressed (see -sam_command_in).
• Filter reads by overlap, length, mapping quality, using a set of user-defined features, or randomly
• Optionally use orientation (strand) of reads and features
• Output
  • Counts: RPKM or TPM in CSV files
  • Profiles
    • Genomic and transcriptomic profiles
    • Using first, last, or all positions (i.e. depth of coverage) in reads or pairs of reads
    • Optionally taking into account mRNA splicing
    • Multiple formats: BedGraph, binary or CSV
• Fast. Implemented in Go using biogo and Go channel-based parallelization

Why?

1. Flexibility: Analyzing standard and novel high-throughput sequencing (HTS) experiments. High-throughput sequencing has been adapted to measure a variety of biological traits in both DNA and RNA. Some methods have become standard such as mRNA-seq, some are becoming standard like Ribo-seq or CLIP-seq, and some require unique and specialized designs, as in MPRAs. GeneAbacus provides many types of count and profile methods adapted for many of these methods. Simultaneously, new profile methods can easily be added using the existing methods as templates.

2. Mapping coordinates: DNA and RNA profiles. HTS experiments are usually mapped to the genome. From the mapped reads, tools providing genome coverage are very useful to generate genomic profiles used for data visualizing in genome browsers. However, while downstream analyses for experiments based on genomic DNA, such as ChIP-seq, can directly use genome coverage of mapped reads, analysis of RNA-based experiments are less straightforward. Most RNAs are strand-specific and/or spliced. Using an algorithm to map coordinates from genomic DNA to RNA, GeneAbacus generates strand-specific RNA profiles. RNA profiles can then be directly used for downstream analysis.

   

3. Flexibility: Output formats. GeneAbacus can export to BedGraph files and in text form (CSV). While BedGraph is widely used, making it appropriate for sharing data, it is not trivial to parse these files efficiently. To overcome this hurdle, GeneAbacus exports to an ad-hoc binary format. The binary format is a simple memory dump, together with a checksum for integrity. This makes it fast and easy to load data for downstream analysis in Python or other environments.

4. Speed: High speed is achieved using Go, parallelization and favoring unsorted over sorted SAM/BAM. To profile and count, GeneAbacus goes through the mapped reads by processing the input SAM/BAM file(s) from beginning to end. This is true for unsorted and sorted SAM/BAMs. GeneAbacus tries to overlap each mapped read (or pair of reads) with a feature. To speed up the overlapping operations, the GeneAbacus core algorithm uses a binary search tree built using the features represented as intervals of coordinates.

   Most tools (such as BEDtools genomecov or mosdepth) require sorted BAM files. This imposes computationally expensive steps to convert SAM files into sorted BAM files using samtools. GeneAbacus avoids these expensive steps by sorting the features instead of the reads. This strategy has two advantages:

   1. Unsorted SAM/BAM files, such as the output from Bowtie2 or STAR, can be used directly without sorting the reads. Sorting reads consumes large amount of RAM.
   2. When pair-end sequencing coverage (number of reads per base) is very high, in MPRA experiments for example, searching for the mate of a read in a pair is computationally expensive due to the design of BAM indices: for each pair, looking for the mate of a read required a search through all the reads mapped at the position of the mate one-by-one. Going through mates for each read in a pair can become the most expensive task.

   Currently, GeneAbacus can use SAM or BAM files containing:

   1. Single-end sorted and unsorted reads or,
   2. Paired-end unsorted reads.

   We recommend using compressed SAM files: they save as much space as BAM files and are processed as fast as BAM files when used with GeneAbacus.

   GeneAbacus is parallelized using Go channels.

Download

See refs page for tarball and executable.

Examples

Read count per transcript (mRNA)

geneabacus -path_bam "input.bam" \
           -path_features "danrer_cdna_all_ensembl104.fon1.json" \
           -read_strand "-" \
           -count_multis "1,900"

Computes read counts and RPKMs for mRNA-seq to counts.csv from input.bam. In this example, the library was prepared using a dUTP protocol that produces antisense first reads to RNAs (set using option -read_strand). Using option -count_multis, two counts are generated per gene: one with reads mapping uniquely ("1") to the genome and one with reads mapping uniquely and those mapping to up to 900 loci (each counting 1/n).

Read count and profile per transcript for Ribo-seq

geneabacus -path_sam "input.sam.zst" \
           -sam_command_in "zstdcat" \
           -path_features "danrer_cdna_protein_coding_rpf_cds_exons_ensembl104.fon1.json" \
           -read_length "28,29" \
           -read_strand "+" \
           -profile_type "first" \
           -profile_multi "900" \
           -profile_norm

The input SAM is compressed using Zstandard. Ribosome-protected fragments (sense to mRNA) of length 28 and 29 nucleotides, mapping a maximum of 900 times in the genome (each counting 1/n) are added to mRNA profiles using the first position of each read. Profiles are normalized to RPM. By default, profiles are output to profiles.bedgraph in BedGraph format. For a more convenient format, see our binary format below which is easy to import into Python for downstream analysis.

Genomic profile per chromosome for ChIP-seq

geneabacus -path_bam "input.bam" \
           -path_features "danrer_genome_all_ensembl_grcz11_chrom_length.tab" \
           -format_features "tab" \
           -profile_type "all" \
           -profile_norm \
           -profile_multi 1 \
           -profile_no_coord_mapping

Genomic profiles are directly viewable as genome browser tracks (default output format is BedGraph). The all profile type generates genomic "coverage". In this case, the profiles are generated for each chromosome (in the tab file). Since chromosomes are the same features used for mapping the reads, there is no need to map coordinates from mapped genomic features to genomic profiles: use -profile_no_coord_mapping to skip this step.

Input

• Mapped reads

  • -path_bam Path to BAM file(s). Multiple files can be specified using a comma separated list.
  • -path_sam Path to SAM file(s). Multiple files can be specified using a comma separated list.
    • -sam_command_in Command line to execute for opening each SAM file (comma separated). For example -sam_command_in zstdcat to open a Zstandard-zipped file (*.sam.szt).
  • -paired for pair-end sequencing. SAM/BAM with paired reads must be unsorted, so that the reads of each pair are next to each other.

• Filtering mapped reads

  • By length
    • -fragment_min_length Minimum fragment length
    • -fragment_max_length Maximum fragment length
    • -read_length Comma separated list of specific read length(s)
  • By mapping quality
    • -read_min_mapping_quality Minimum read mapping quality (5th column in SAM, MAPQ)
    • -read_in_proper_pair Only read in proper pairs (default: all pairs) (2nd column in SAM, 0x2 flag)
  • Proportion
    • -rand_proportion Randomly select a proportion of all reads (from 0. to 1.). 0.5 will keep 50% of the reads/pairs.
  • Overlap with features
    • -read_min_overlap Minimum total overlap of the read with the feature interval(s) (default 10) to be counted and included into the profile.

• Library

  • -read_strand Specify strandness of the sequenced library by setting the orientation of read 1, i.e. + or - or unstranded if empty.

• Features

  • -path_features Path to features file
  • -format_features FON (default) FON format Within FON, specify where to find the name of the features, their chromosome, coordinate list (such as exons), and strand:
    • -fon_name FON key for feature name (default "transcript_stable_id")
    • -fon_chrom FON key for chromosome or locus (default "chrom")
    • -fon_coordsFON key for coordinates (exons for example) (default "exons")
    • -fon_strand FON key for strand (default "strand")
  • -format_features tab Tabulated file
    • -feature_strand Default feature strand (default "+")
  • -path_mapping Path to feature name(s) mapping (tabulated file). For example, if features are chromosomes, this file can be used to translate chromosome/contig names from Ensembl to UCSC names.

• Filtering features. Filter input reads. Only reads mapping to features within the filter will be further processed. All options have the same name of the main features with the added suffix _filter. For example, filter features can be used to filter out reads from a Ribo-seq experiment that are not mapped to exons. Remaining reads can then be counted within coding-sequences (CDS) using -format_features.

  • -path_features_filter Path to filtering features file
  • -format_features_filter FON (default) FON format Within FON, specify where to find the name of the features, their chromosome, coordinate list (such as exons), and strand:
    • -fon_name_filter FON key for feature name (default "transcript_stable_id")
    • -fon_chrom_filter FON key for chromosome or locus (default "chrom")
    • -fon_coords_filterFON key for coordinates (exons for example) (default "exons")
    • -fon_strand_filter FON key for strand (default "strand")
  • -format_features_filter tab Tabulated file
    • -feature_strand_filter Default feature strand (default "+")
  • -include_missing_in_filter Common use cases of filter require each feature to be in the main (specified with -path_features) and the filter (specified with -path_features_filter) features. By default, GeneAbacus will check that each feature from the main can also be found in the filter using the feature name. -include_missing_in_filter removes this check allowing for features to be absent in the filter.

Output

• -ignore_nh_tag Software mapping reads, notably RNA such as STAR, add an NH tag in the optional fields of SAM files. The NH tag holds the total number of hits. With this option, any NH tag will be ignored and all alignments will be considered unique (i.e. NH=1).
• -path_report Write a report to this path (stdout with -)
• -path_sam_out Path to saved SAM file containing read(s) included in counts or profiles
• -append Instead of creating new count and/or profile files and eventually overwriting existing files, this option will open existing files using APPEND mode, and append content at the end of existing files.

Count

• Multiplicity
  • -count_multis Multiple counts can be generated including reads/pairs mapping uniquely ("1") to the genome or not. A read multiplicity of "2" will include reads/pairs mapping uniquely and those mapping to two loci. Multiple counts are specified using a comma separated list (default "1,2,900").
• Total
  • -count_totals Totals used for normalization such as computing RPKM are calculated by the program. If desired, totals can be specified by the user as comma separated list of totals. This list must have the same number of totals as multiplicity in the -count_multis list.
  • -count_total_real_read Totals used for normalization such as computing RPKM are calculated as the number of alignments intersecting with the features. Each alignment is weighted by their multiplicity (number of hits for the read from the NH tag) so that a read will count 1/NH for each alignment. While this approach is acceptable, this calculation is an approximation: the NH tag is computed genome-wide while most counts are not (on the transcriptome for example). The -count_total_real_read option calculates the real total number of reads by counting the reads intersecting the features using their name. Be aware, this option requires large amounts of RAM.
• -count_path Path to counts output (default counts.csv)
• -count_in_profile Only count reads included in the profiles

Profile

• -profile_paths Path to profile output(s) (comma separated) (default profiles.bedgraph)
• -profile_formats Profile output format. Available formats are bedgraph, binary' or csv (default bedgraph). Multiple formats can be set as comma separated list. The number of formats and output paths (in -profile_paths) must be the same.
• -profile_multi Maximum alignment multiplicity to include a read in the profile (default 900). See -count_multis for details.
• -profile_norm By default, profiles contains the number of reads per nucleotide. With -profile_norm, profile counts are normalized using total reads to RPM.
• -profile_no_coord_mapping Skip coordinate mapping from input to feature to speed things up. This option requires input reads and features to be within the same coordinate system (for example reads mapped to a genome and features being chromosomes). It only produces profile in the same orientation as the input and convenient to generate genomic profiles.
• -profile_overhang Overhang length to add to each side of the profiles

Profile type

• -profile_type Profile type: first, last, first-last, position, all, all-extension or all-slice

  

• first

  • -profile_no_untemplated Include only reads w/o untemplated nucleotide in the profile
  • -profile_untemplated Remove maximum untemplated nucleotides

• position

  • -profile_position_fraction Fraction of position between start and end for position profile (default 0.5)

• all-extension

  • -profile_extension_length Extension length

Other options

• -num_worker Number of worker(s) to run in parallel (default 1)
• -verbose Verbose (adapt how much verbose is the output using -verbose_level)
• -version Print version and quit

Profile binary format

The profile binary format consists of a header followed by the profile of each feature concatenated together. Data is stored as a raw sequence of bytes (in little-endian order) in a file with the .bin extension.

In the current version (3), the header contains 3 fields:

1. A version number stored as uint8 (or byte)
2. The total length of all profiles added together stored as uint32
3. A checksum for the profiles length computed with Adler-32 stored as uint32 (see below).

How to split the concatenated profiles into individual profiles is not included in the binary file. A list of the length of each profile from a FON1, BED, or TAB file is required. A checksum insures that the same list of lengths is used at the creation of the binary file and when reading it. The checksum is computed by concatenating the lengths of each profile (uint32) into a raw sequence of bytes, of which an Adler-32 checksum is calculated.

For reducing storage requirements, binary files are compressed using LZ4. Since most genomic profiles usually have many zeros, LZ4 offers fast decompression speed and a high compression rate. Other or no algorithm can easily be employed. Using this compression, binary profiles are not indexed and are intended to be fully loaded into RAM to be used in downstream analysis.

Reading profiles from Python

import geneabacus.profileio
profiles = geneabacus.profileio.pfopen('profiles.bin.lz4', 'danrer_cdna_protein_coding_ensembl104.fon1.json')

To get a transcript profile:

profiles['ENSDART00000000486']
# will return
array([0., 0., 21., ..., 0., 3., 0.], dtype=float32)

License

GeneAbacus is distributed under the Mozilla Public License Version 2.0 (see /LICENSE).

Copyright © 2015-2023 Charles E. Vejnar