AlignGraph2: similar genome-assisted reassembly pipeline for PacBio long reads

Draft genome of the aardaker (Lathyrus tuberosus L.), a tuberous legume

OBJECTIVES

Lathyrus tuberosus is a nitrogen-fixing member of the Fabaceae which forms protein-rich tubers. To aid future domestication programs for this legume plant and facilitate evolutionary studies of tuber formation, we have generated a draft genome assembly based on Pacific Biosciences sequence reads.

DATA DESCRIPTION

Genomic DNA from L. tuberosus was sequenced with PacBio's HiFi sequencing chemistry generating 12.8 million sequence reads with an average read length of 14 kb (approximately 180 Gb of sequence data). The reads were assembled to give a draft genome of 6.8 Gb in 1353 contigs with an N50 contig length of 11.1 Mb. The GC content of the genome assembly was 38.3%. BUSCO analysis of the genome assembly indicated a genome completeness of at least 96%. The genome sequence will be a valuable resource, for example, in assessing genomic consequences of domestication efforts and developing marker sets for breeding programs. The L. tuberosus genome will also aid in the analysis of the evolutionary history of plants within the nitrogen-fixing Fabaceae family and in understanding the molecular basis of tuber evolution.

Immunoglobulin Classification Based on FC* and GC* Features

Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.

Application of Sparse Representation in Bioinformatics

Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no “overfitting” phenomenon, the application of sparse representation in bioinformatics should be studied further. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.