Alignment-free Genomic Analysis via a Big Data Spark Platform

NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions

Accurate identification of ncRNA-protein interactions (NPIs) is critical for understanding various cellular activities and biological functions of ncRNAs and proteins. Many sequence- and/or structure- and graph-based computational approaches have been developed to identify NPIs from large-scale ncRNA and protein data in a high-throughput manner. However, many sequence- and/or structure- and graph-based computational approaches often ignore either the topological information in NPIs or the influence of other molecule networks on NPI prediction. In this work, we propose NPI-HGNN, an end-to-end graph neural network (GNN)-based approach for the identification of NPIs from a large heterogeneous network, consisting of the ncRNA-protein interaction network, the ncRNA-ncRNA similarity network, and the protein-protein interaction network. To our knowledge, NPI-HGNN is the first GNN-based predictor that integrates related heterogeneous networks for NPI prediction. Experiments on five benchmarking datasets demonstrate that NPI-HGNN outperformed several state-of-the-art sequence- and/or structure- and graph-based predictors. In addition, we showcased the prediction power of NPI-HGNN by identifying 12 interacting ncRNAs of the pre-mRNA 3' end processing protein, which indicates the effectiveness of the proposed model. The source code of NPI-HGNN is freely available for academic purposes at https://github.com/zhangxin11111/NPI-HGNN .

Application of Feature Definition and Quantification in Biological Sequence Analysis

Biological sequence analysis is the most fundamental work in bioinformatics. Many research methods have been developed in the development of biological sequence analysis. These methods include sequence alignment-based methods and alignment-free methods. In addition, there are also some sequence analysis methods based on the feature definition and quantification of the sequence itself. This editorial introduces the methods of biological sequence analysis and explores the significance of defining features and quantitative research of biological sequences.

Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment

Some scientific studies involve huge amounts of bioinformatics data that cannot be analyzed on personal computers usually employed by researchers for day-to-day activities but rather necessitate effective computational infrastructures that can work in a distributed way. For this purpose, distributed computing systems have become useful tools to analyze large amounts of bioinformatics data and to generate relevant results on virtual environments, where software can be executed for hours or even days without affecting the personal computer or laptop of a researcher. Even if distributed computing resources have become pivotal in multiple bioinformatics laboratories, often researchers and students use them in the wrong ways, making mistakes that can cause the distributed computers to underperform or that can even generate wrong outcomes. In this context, we present here ten quick tips for the usage of Apache Spark distributed computing systems for bioinformatics analyses: ten simple guidelines that, if taken into account, can help users avoid common mistakes and can help them run their bioinformatics analyses smoothly. Even if we designed our recommendations for beginners and students, they should be followed by experts too. We think our quick tips can help anyone make use of Apache Spark distributed computing systems more efficiently and ultimately help generate better, more reliable scientific results.

KINN: An alignment-free accurate phylogeny reconstruction method based on inner distance distributions of k-mer pairs in biological sequences

Alignment-based methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational complexity. Alignment-free methods for sequence comparison and phylogeny inference have attracted a great deal of attention in recent years. Here, we explore an alignment-free approach that uses inner distance distributions of k-mer pairs in biological sequences for phylogeny inference. For every sequence in a dataset, our method transforms the sequence into a numeric feature vector consisting of features each representing a specific k-mer pair's contribution to the characterization of the sequentiality uniqueness of the sequence. This newly defined k-mer pair's contribution is an integration of the reverse Kullback-Leibler divergence, pseudo mode and the classic entropy of an inner distance distribution of the k-mer pair in the sequence. Our method has been tested on datasets of complete genome sequences, complete protein sequences, and gene sequences of rRNA of various lengths. Our method achieves the best performance in comparison with state-of-the-art alignment-free methods as measured by the Robinson-Foulds distance between the reference and the constructed phylogeny trees.

The power of word-frequency-based alignment-free functions: a comprehensive large-scale experimental analysis

MOTIVATION

Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e. their ability to identify true similarity, has been limited to some members of the D2 family. The corresponding experimental studies have concentrated on short sequences, a scenario no longer adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited.

RESULTS

By concentrating on a representative set of word-frequency-based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the 15 functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public.

AVAILABILITY AND IMPLEMENTATION

The software is available at: https://github.com/pipp8/power_statistics.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.