SWALO: scaffolding with assembly likelihood optimization

Metagenome-Assembled Genome of "Candidatus Aramenus sp. CH1" from the Chichon volcano, Mexico

The Chichon volcano contains several thermal manifestations including an acidic crater lake. Here we report a metagenome-assembled genome of "Candidatus Aramenus sp. CH1," a Sulfolobales archaeon inhabiting the crater lake from the Chichon volcano. In this study, we generated a novel Aramenus genome sequence from a thermal area in Southern Mexico.

SLHSD: hybrid scaffolding method based on short and long reads

In genome assembly, scaffolding can obtain more complete and continuous scaffolds. Current scaffolding methods usually adopt one type of read to construct a scaffold graph and then orient and order contigs. However, scaffolding with the strengths of two or more types of reads seems to be a better solution to some tricky problems. Combining the advantages of different types of data is significant for scaffolding. Here, a hybrid scaffolding method (SLHSD) is present that simultaneously leverages the precision of short reads and the length advantage of long reads. Building an optimal scaffold graph is an important foundation for getting scaffolds. SLHSD uses a new algorithm that combines long and short read alignment information to determine whether to add an edge and how to calculate the edge weight in a scaffold graph. In addition, SLHSD develops a strategy to ensure that edges with high confidence can be added to the graph with priority. Then, a linear programming model is used to detect and remove remaining false edges in the graph. We compared SLHSD with other scaffolding methods on five datasets. Experimental results show that SLHSD outperforms other methods. The open-source code of SLHSD is available at https://github.com/luojunwei/SLHSD.

Figbird: A probabilistic method for filling gaps in genome assemblies

MOTIVATION

Advances in sequencing technologies have led to the sequencing of genomes of a multitude of organisms. However, draft genomes of many of these organisms contain a large number of gaps due to the repeats in genomes, low sequencing coverage and limitations in sequencing technologies. Although there exist several tools for filling gaps, many of these do not utilize all information relevant to gap filling.

RESULTS

Here, we present a probabilistic method for filling gaps in draft genome assemblies using second generation reads based on a generative model for sequencing that takes into account information on insert sizes and sequencing errors. Our method is based on the expectation-maximization (EM) algorithm unlike the graph based methods adopted in the literature. Experiments on real biological datasets show that this novel approach can fill up large portions of gaps with small number of errors and misassemblies compared to other state of the art gap filling tools.

AVAILABILITY AND IMPLEMENTATION

The method is implemented using C ++ in a software named "Filling Gaps by Iterative Read Distribution (Figbird)", which is available at: https://github.com/SumitTarafder/Figbird.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.