Abstract
Sequence assembling is an important step for bioinformatics study. With the help of next generation sequencing (NGS) technology, high throughput DNA fragment (reads) can be randomly sampled from DNA or RNA molecular sequence. However, as the positions of reads being sampled are unknown, assembling process is required for combining overlapped reads to reconstruct the original DNA or RNA sequence. Compared with traditional Sanger sequencing methods, although the throughput of NGS reads increases, the read length is shorter and the error rate is higher. It introduces several problems in assembling. Moreover, paired-end reads instead of single-end reads can be sampled which contain more information. The existing assemblers cannot fully utilize this information and fails to assemble longer contigs. In this article, we will revisit the major problems of assembling NGS reads on genomic, transcriptomic, metagenomic and metatranscriptomic data. We will also describe our IDBA package for solving these problems. IDBA package has adopted several novel ideas in assembling, including using multiple k, local assembling and progressive depth removal. Compared with existence assemblers, IDBA has better performance on many simulated and real sequencing datasets.
Highlights
Deoxyribonucleic acid (DNA) is a sequence of nucleotides adenine (A), cytosine (C), guanine (G) and thymine (T) which is used to encode all genetic information for controlling development and functioning of most organisms in the world except some virus
Assembling step analyzes the set of fragments sampled from unknown locations and determines the original DNA or ribonucleic acid (RNA) sequence
We will explain the general problems of assembling next generation sequencing (NGS) data (Section 2) and the possible solutions introduced by IDBA package (Section 3)
Summary
Since the location of each read in the DNA or RNA sequence is unknown, assembling process is needed to combine these reads into the original sequence. There are three major approaches for assembling reads: (i) overlap-and-extend, (ii) string graph, and (iii) de Bruijn graph. Extension of IDBA for assembling prokaryotic metatranscriptomic data It assembles reads by applying known protein reference sequences. Similar to the overlap-and-extend approach, since the data structure used for storing the string graph is large, it requires a large amount of memory and takes a long time for finding overlapped reads. When all reads are error-free and the number of sequenced reads is large compared with the genome length (high sequencing depth), both string graph and de Bruijn graph approaches work well. Because of the existence of erroneous reads and the repeated patterns exist in the genome, these two approaches may not perform well on some sequencing data
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