centroFlye: Assembling Centromeres with Long Error-Prone Reads

Abstract

Although variations in centromeres have been linked to cancer and infertility, centromeres still represent the "dark matter of the human genome" and remain an enigma for both biomedical and evolutionary studies. Since centromeres have withstood all previous attempts to develop an automated tool for their assembly and since their assembly using short reads is viewed as intractable, recent efforts attempted to manually assemble centromeres using long error-prone reads. We describe the centroFlye algorithm for centromere assembly using long error-prone reads, apply it for assembling the human X centromere, and use the constructed assembly to gain insights into centromere evolution. Our analysis reveals putative breakpoints in the previous manual reconstruction of the human X centromere and opens a possibility to automatically close the remaining multi-megabase gaps in the reference human genome.

Identification and Genotyping of Transposable Element Insertions From Genome Sequencing Data

“…Despite the improved performance of TE detection algorithms for short‐read sequencing data, it is still difficult to detect certain subsets of TE insertions, including those that accompany complex genomic rearrangements or fall into repetitive genomic regions. Genomic regions with existing TE copies from the same TE subfamily, or centromeric or telomeric regions with many gaps, are particularly challenging for TE detection due to limited short read mappability (Bzikadze & Pevzner, 2019; Jain et al., 2018; Miga et al., 2019). Recent advances in long‐read sequencing, notably PacBio and Oxford Nanopore (ONT) technologies, create ∼10‐ to 15‐Kbp‐long reads.…”

The String Decomposition Problem and its Applications to Centromere Assembly

“…We benchmarked various approaches to string decomposition using centromeric reads from chromosome X since this centromere (referred to as cenX) was recently assembled, thus providing the ground truth for our benchmarking. This benchmarking utilized 2,680 reads (total read length 132,9 Mb) that were recruited to cenX in Bzikadze and Pevzner, 2019). monomers and HOR sequences on cenX.…”

“…monomers and HOR sequences on cenX. We used the cenX HOR consensus sequence DXZ1* derived in Bzikadze and Pevzner, 2019. Appendix " Extracting monomers from DXZ1* " describes decomposition of DXZ1* into twelve monomers using Alpha-CENTAURI (Sevim et al, 2016).…”

“…In order to extract twelve monomer sequences we run "chop_to_monomers.py" script from Alpha-CENTAURI v.0.2G on the consensus monomer HMM ( https://github.com/volkansevim/alpha-CENTAURI/blob/master/example/alpha.hmm ) and a concatenation of two DXZ1* sequences derived in Bzikadze and Pevzner, 2019. The twelve cenX monomers are derived from the Alpha-CENTAURI output.…”

“…The initial attempts to assemble ETRs (Jain et al, 2018;Bzikadze and Pevzner, 2019;Miga et al, 2019) revealed the importance of the String Decomposition Problem , partitioning an ETR (or an error-prone long read sampled from an ETR) into repetitive units forming these repeats. Although no existing tool explicitly addresses the String Decomposition Problem, Tandem Repeats Finder (Benson, 1999) , PERCON (Kazakov et al, 2003), Alpha-CENTAURI (Sevim et al, 2016) , and the Noise-Cancelling Repeat Finder (Harris et al, 2019) address related problems.…”