scholarly journals Three invariant Hi-C interaction patterns: applications to genome assembly

2018 ◽  
Author(s):  
Sivan Oddes ◽  
Aviv Zelig ◽  
Noam Kaplan

AbstractAssembly of reference-quality genomes from next-generation sequencing data is a key challenge in genomics. Recently, we and others have shown that Hi-C data can be used to address several outstanding challenges in the field of genome assembly. This principle has since been developed in academia and industry, and has been used in the assembly of several major genomes. In this paper, we explore the central principles underlying Hi-C-based assembly approaches, by quantitatively defining and characterizing three invariant Hi-C interaction patterns on which these approaches can build: Intrachromosomal interaction enrichment, distance-dependent interaction decay and local interaction smoothness. Specifically, we evaluate to what degree each invariant pattern holds on a single locus level in different species, cell types and Hi-C map resolutions. We find that these patterns are generally consistent across species and cell types but are affected by sequencing depth, and that matrix balancing improves consistency of loci with all three invariant patterns. Finally, we overview current Hi-C-based assembly approaches in light of these invariant patterns and demonstrate how local interaction smoothness can be used to easily detect scaffolding errors in extremely sparse Hi-C maps. We suggest that simultaneously considering all three invariant patterns may lead to better Hi-C-based genome assembly methods.

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 401
Author(s):  
Jon Bråte ◽  
Janina Fuss ◽  
Kjetill S. Jakobsen ◽  
Dag Klaveness

Hydrurus foetidus is a freshwater alga belonging to the phylum Heterokonta. It thrives in cold rivers in polar and high alpine regions. It has several morphological traits reminiscent of single-celled eukaryotes, but can also form macroscopic thalli. Despite its ability to produce polyunsaturated fatty acids, its life under cold conditions and its variable morphology, very little is known about its genome and transcriptome. Here, we present an extensive set of next-generation sequencing data, including genomic short reads from Illumina sequencing and long reads from Nanopore sequencing, as well as full length cDNAs from PacBio IsoSeq sequencing and a small RNA dataset (smaller than 200 bp) sequenced with Illumina. We combined this data with, to our knowledge, the first draft genome assembly of a chrysophyte algae. The assembly consists of 5069 contigs to a total assembly size of 171 Mb and a 77% BUSCO completeness. The new data generated here may contribute to a better understanding of the evolution and ecological roles of chrysophyte algae, as well as to resolve the branching patterns within the Heterokonta.


PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e62856 ◽  
Author(s):  
Yen-Chun Chen ◽  
Tsunglin Liu ◽  
Chun-Hui Yu ◽  
Tzen-Yuh Chiang ◽  
Chi-Chuan Hwang

Author(s):  
Anne Krogh Nøhr ◽  
Kristian Hanghøj ◽  
Genis Garcia Erill ◽  
Zilong Li ◽  
Ida Moltke ◽  
...  

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.


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