RFfiller: a robust and fast statistical algorithm for gap filling in draft genomes - Firaol Dida Midekso; Gangman Yi (PeerJ, 2022)
Numerous published genomes contain gaps or unknown sequences. Gap filling is a critical final step in de novo genome assembly, particularly for large genomes. While certain computational approaches partially address the problem, others have shortcomings regarding the draft genome’s dependability and correctness (high rates of mis-assembly at gap-closing sites and high error rates). While it is well established that genomic repeats result in gaps, many sequence reads originating from repeat-related gaps are typically missed by existing approaches. A fast and reliable statistical algorithm for closing gaps in a draft genome is presented in this paper. It utilizes the alignment statistics between scaffolds, contigs, and paired-end reads to generate a Markov chain that appropriately assigns contigs or long reads to scaffold gap regions (only corrects candidate regions), resulting in accurate and efficient gap closure. To reconstruct the missing component between the two ends of the same insert, the RFfiller meticulously searches for valid overlaps (in repeat regions) and generates transition tables for similar reads, allowing it to make a statistical guess at the missing sequence. Finally, in our experiments, we show that the RFfiller’s gap-closing accuracy is better than that of other publicly available tools when sequence data from various organisms are used. Assembly benchmarks were used to validate RFfiller. Our findings show that RFfiller efficiently fills gaps and that it is especially effective when the gap length is longer. We also show that the RFfiller outperforms other gap closing tools currently on the market.