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Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field. [ABSTRACT FROM AUTHOR]

A sensitive and convenient method for the detection of epidermal growth factor receptor (EGFR) T790M mutation in non-small cell lung cancer (NSCLC) patients with acquired resistance to tyrosine kinase inhibitors (TKIs) would be desirable to guide treatment strategy. Consequently, studies have focused on sensitive characterization of EGFR T790M mutation. Herein, two methods of co-amplification at lower denaturation temperature PCR (COLD-PCR) and pyrosequencing were combined (COLDPCR/ pyrosequencing) for detecting EGFR T790M mutation. Evaluation of mutation-containing dilutions revealed that the sensitivities of COLD-PCR/pyrosequencing and conventional PCR/pyrosequencing assays for the detection of the T790M mutation were 0.1 and 5%, respectively, indicating a 50-fold increase in sensitivity. When the T790M mutation in 20 clinical NSCLC samples who had relapsed under firstgeneration EGFR TKI were further determined using COLD-PCR/pyrosequencing and conventional PCR/pyrosequencing, the detection rates were 35% (7/20) and 25% (5/20), respectively. All patients who were positive for the T790M mutation with conventional PCR/pyrosequencing were also found to be positive with COLD-PCR/pyrosequencing. The discordant cases were 2 samples with no T790M mutation detected with conventional PCR/pyrosequencing, but which were positive with COLD-PCR/pyrosequencing. COLD-PCR/pyrosequencing is a sensitive and cost-effective tool for detecting the T790M mutation which will permit an improvement of therapeutic management.

We proposed a novel method for the identification of pathogenic bacteria, in which a partial amplicon of the molecular markers was targeted by using pyrosequencing with di-base addition (PDBA). PDBA was conducted by synchronously adding di-base instead of one base into a reaction, and a set of highly sequence-specific encodings containing the type and the number of incorporated nucleotide(s) (peak height) were obtained. By comparing the encoding sizes of each isolate and the number of incorporated nucleotide(s) in each cycle, moving from first to last, various kinds of bacteria could be unambiguously identified. To verify its feasibility, we simulated PDBA results from thirteen isolates of Mycobacterium species and compared their encoding sizes and the number of incorporated nucleotide(s) in each cycle with those predicted by a homemade software. The thirteen isolates were successfully differentiated. We also targeted partial RNase P RNA gene (rnpB) of cultured M. paratubercuosis and M. celatum to differentiate the two isolates. By comparing the encoding size of each isolate and the number of incorporated nucleotide(s) in each cycle, the two Mycobacterium isolates were successfully differentiated. In conclusion, PDBA enabled to reliably identify pathogenic bacteria.

Data-independent acquisition (DIA) is a high-throughput, unbiased mass spectrometry data acquisition method which has good quantitative reproducibility and is friendly to low-abundance proteins. It becomes the preferred choice for clinical proteomic studies especially for large cohort studies in recent years. The mass-spectrometry (MS)/MS spectra generated by DIA is usually heavily mixed with fragment ion information of multiple peptides, which makes the protein identification and quantification more difficult. Currently, DIA data analysis methods fall into two main categories, namely peptide-centric and spectrum-centric. The peptide-centric strategy is more sensitive for identification and more accurate for quantification. Thus, it has become the mainstream strategy for DIA data analysis, which includes four key steps: building a spectral library, extracting ion chromatogram, feature scoring and statistical quality control. This work reviews the peptide-centric DIA data analysis procedure, introduces the corresponding algorithms and software tools, and summarizes the improvements for the existing algorithms. Finally, the future development directions are discussed.

Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.

Cancer is a heterogeneous disease with complex mechanisms that requires targeted precision medicine strategies. The growth of precision medicine is indispensable from the rapid development of genomics. However, genomics has certain limitations in molecular phenotype analysis, proteogenomics thus arose at the right time. Proteogenomics is the merging of proteomics and genomics. This review describes the limitations of genomic analysis and highlights the importance of proteogenomics to re-understand precision oncology from a proteogenomic perspective. In addition, the application of proteogenomics in precision oncology is briefly introduced, the related public data projects are described, and finally, the challenges that need to be addressed at this stage are proposed.

Mass spectrometry and database searching are necessary to identify proteins and peptides. With the rapid development of mass spectrometry technology, mass spectrometry data in proteomics are acquired very quickly, providing a powerful method to identify large-scale proteins and peptides, making mass spectrometry data-based proteomics research more and more into the mainstream. The traditional database searching method has many limitations to identify post-translational modifications of peptides. This paper systematically reviews the development, theoretical concept and applications of spectral network method, and the advantages of spectral network library to identify peptides.

Exponential growth of the mass spectrometry (MS) data is exhibited when the mass spectrometry-based proteomics has been developing rapidly. It is a great challenge to develop some quick, accurate and repeatable methods to identify peptides and proteins. Nowadays, the spectral library searching has become a mature strategy for tandem mass spectra based proteins identification in proteomics, which searches the experiment spectra against a collection of confidently identified MS/MS spectra that have been observed previously, and fully utilizes the abundance in the spectrum, peaks from non-canonical fragment ions, and other features. This review provides an overview of the implement of spectral library search strategy, and two key steps, spectral library construction and spectral library searching comprehensively, and discusses the progress and challenge of the library search strategy.

Phylogenomics is a new phylogenetic field that aims to rebuild phylogenetic relationship of organisms using whole genome data. It can effectively eliminate the impact of horizontal gene transfer and variant evolutionary rates on phylogeny. According to the genome data type they are based on, these methods can be classified into five groups: multi-gene based, gene content based, gene order based, K-string based, and metabolic pathway based. The mechanism, speed, accuracy, applicable range and their application of these methods are summarized. The prospects of phylogenomics and challenges that it is faced with are also discussed.