racon | Ultrafast consensus module for raw de novo genome assembly | Genomics library

racon is a C++ library typically used in Artificial Intelligence, Genomics applications. racon has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

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 short accurate data or data produced by third generation of sequencing. The type of data inputted is automatically detected. Although, Racon expects single-end short reads, while paired-end reads should be renamed with unique names up to the first whitespace and joined into a single file before mapping (which can be done with misc/racon_preprocess.py). 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.