Anchorage

The Anchorage algorithm is a method to assemble synthetic long reads (SLR) with anchors. Anchors are known short sequences ligated to both ends of a sequenced molecule. It is applicable to LoopSeq, LoopSeq Solo, and other sequencing technologies which have known sequences at the end points of a molecule.

Installation

Anchorage can be installed by cloning this repository.

git clone https://github.com/Shao-Group/anchorage.git

Anchoarge is implemented in python3. Anchoarge requires kmc3 and seqkit and is dependent on python package networkx and numpy. If desired, users may install the dependencies separately and manually, but all executables and libraries should be callable from $PATH.

Otherwise, an easier way to install and use those dependencies is via conda with the .yml file .

conda env create -f src/python/environment.yml
conda activate anchorage

Usage

usage: python anchorage.py -s1 -s2 -r1 -r2 [options]

Required arguments:
  -s1, --anchor_start       sequence of start anchor
  -s2, --anchor_end         sequence of end anchor
  -r1, --read1_fq           read1 file of fastq/fasta 
  -r2, --read2_fq           read2 file of fastq/fasta

optional arguments:
  -h, --help                 show this help message and exit
  -o, --output_prefix        output file prefix, default: anchorage_contig
  -k                         a series of k-mer sizes, default 21,33,55,77,99.
  -t, --threads              number of threads for spades
  --contig_barcode_len       length of contig barcode (for LoopSeq Solo)
  --no-trim_barcode          NOT trimming contig barcode from contig
  --max_nm_anchors           maximum number of mismatches/indels in anchors permitted
  --verbose                  

Additional information for MacOS

run ulimit -n 2048 before running anchorage. This is needed by kmc. See refresh-bio/KMC#140.

Example

We provided an example dataset and its ground truth sequence in the example directory. The reads are simulated using NG_042068.1.fa as a reference and added CGCAGAGTACAT/TTGGAGTTAAAG as the start/end anchors.

Users can assemble this SLR by using:

ulimit -n 2048  # for MacOS

python anchorage.py -s1 CGCAGAGTACAT -s2 TTGGAGTTAAAG -r1 example/sample_01_1.fasta -r2 example/sample_01_2.fasta  --no-trim_barcode --contig_barcode_len 0 -o anchorage-example

The assembled fasta sequence will be in anchorage-example.fa

License

Anchorage is available under a BSD-3-Clause license. This repository provides only the anchorage assembly algorithm and parts of its code are forked from loop-core (see loop-core's LICENSE). For the complete LoopSeq data analysis pipeline, including trimming, demultiplex, read grouping by UMI, assembly, aggregation of results, please refer to loop-core.

Citation

If you find this repository useful, please consider cite:

Xiaofei Carl Zang, Xiang Li, Kyle Metcalfe, Tuval Ben-Yehezkel, Ryan Kelley, and Mingfu Shao. Anchorage Accurately Assembles Anchor-Flanked Synthetic Long Reads. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 22:1-22:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.WABI.2024.22