Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer\'s disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

Pectate lyases and pectin lyases have essential roles in various biotechnological applications, such as textile industry, paper making, pectic wastewater pretreatment, juice clarification and oil extraction. They can effectively cleave the α-1,4-glycosidic bond of pectin molecules back bone by β-elimination reaction to produce pectin oligosaccharides. In this way, it will not generate highly toxic methanol and has the advantages of good enzymatic selectivity, less by-products, mild reaction conditions and high efficiency. However, numerous researches have been done for several decades; there are still no comprehensive reviews to summarize the recent advances of pectate lyases and pectin lyases. This review tries to fill this gap by providing all relevant information, including the substrate, origin, biochemical properties, sequence analysis, mode of action, the three-dimensional structure and catalytic mechanism.

Telomeres are located at the ends of chromosomes and have specific sequences with a distinctive structure that safeguards genes. They possess capping structures that protect chromosome ends from fusion events and ensure chromosome stability. Telomeres shorten in length during each cycle of cell division. When this length reaches a certain threshold, it can lead to genomic instability, thus being implicated in various diseases, including cancer and neurodegenerative disorders. The possibility of telomeres serving as a biomarker for aging and age-related disease is being explored, and their significance is still under study. This is because post-mitotic cells, which are mature cells that do not undergo mitosis, do not experience telomere shortening due to age. Instead, other causes, for example, exposure to oxidative stress, can directly damage the telomeres, causing genomic instability. Nonetheless, a general agreement has been established that measuring telomere length offers valuable insights and forms a crucial foundation for analyzing gene expression and epigenetic data. Numerous approaches have been developed to accurately measure telomere lengths. In this review, we summarize various methods and their advantages and limitations for assessing telomere length.

Noroviruses are among the major causative agents of human acute gastroenteritis, and the nature of norovirus outbreaks can differ considerably. The number of single-nucleotide polymorphisms (SNPs) between strains is used to assess their relationships. There is currently no universally accepted cutoff value for clustering strains that define an outbreak or linking the individuals involved. This study was conducted to estimate the threshold value of genomic variations among related strains within norovirus outbreaks. We carried out a literature search in the PubMed and Web of Science databases. SNP rates were defined as the number of SNPs/sequence length (bp) × 100%. The Mann-Whitney U-test was used in comparisons of the distribution of SNP rates for different sequence regions, genogroups (GI and GII), transmission routes, and sequencing methods. A total of 25 articles reporting on 108 norovirus outbreaks were included. In 99.1% of the outbreaks, the SNP rates were below 0.50%, and in 89.8%, the SNP rates were under 0.20%. Outbreak strains showed higher SNP rates when the P2 domain was used for sequence analysis (Z = -2.652, p = 0.008) and when an NGS method was used (Z = -3.686, p < 0.001). Outbreaks caused by different norovirus genotypes showed no significant difference in SNP rates. Compared with person-to-person outbreaks, SNP rates were lower in common-source outbreaks, but no significant difference was found when differences in sequencing methods were taken into consideraton. SNP rates under 0.20% and 0.50% could be considered as the rigorous and relaxed threshold, respectively, of strain similarity within a norovirus outbreak. More data are needed to evaluate differences within and between various norovirus outbreaks.

OBJECTIVE: This systematic review aims to elucidate the methodological practices and reporting standards associated with sequence analysis (SA) for the identification of clinical pathways in real-world scenarios, using routinely collected data.
METHODS: We conducted a methodological systematic review, searching five medical and health databases: MEDLINE, PsycINFO, CINAHL, EMBASE and Web of Science. The search encompassed articles from the inception of these databases up to February 28, 2023. The search strategy comprised two distinctive sets of search terms, specifically focused on sequence analysis and clinical pathways.
RESULTS: 19 studies met the eligibility criteria for this systematic review. Nearly 60% of the included studies were published in or after 2021, with a significant proportion originating from Canada (n = 7) and France (n = 5). 90% of the studies adhered to the fundamental SA steps. The optimal matching (OM) method emerged as the most frequently employed dissimilarity measure (63%), while agglomerative hierarchical clustering using Ward\'s linkage was the preferred clustering algorithm (53%). However, it is imperative to underline that a majority of the studies inadequately reported key methodological decisions pertaining to SA.
CONCLUSIONS: This review underscores the necessity for enhanced transparency in reporting both data management procedures and key methodological choices within SA processes. The development of reporting guidelines and a robust appraisal tool tailored to assess the quality of SA would be invaluable for researchers in this field.

The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.

The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.

MicroRNAs (miRNAs) have been implicated in childhood acute lymphoblastic leukemia (ALL) pathogenesis. We performed a systematic review and meta-analysis of miRNA single-nucleotide polymorphisms (SNPs) in childhood ALL compared with healthy children, which revealed (i) that the CC genotype of rs4938723 in pri-miR-34b/c and the TT genotype of rs543412 in miR-100 confer protection against ALL occurrence in children; (ii) no significant association between rs2910164 genotypes in miR-146a and childhood ALL; and (iii) SNPs in DROSHA, miR-449b, miR-938, miR-3117 and miR-3689d-2 genes seem to be associated with susceptibility to B-ALL in childhood. A review of published literature on differential expression of miRNAs in children with ALL compared with controls revealed a significant upregulation of the miR-128 family, miR-130b, miR-155, miR-181 family, miR-210, miR-222, miR-363 and miR-708, along with significant downregulation of miR-143 and miR-148a, seem to have a definite role in childhood ALL development. MicroRNA signatures among childhood ALL subtypes, along with differential miRNA expression patterns between B-ALL and T-ALL cases, were scrutinized. With respect to T-ALL pediatric cases, we reanalyzed RNA-seq datasets with a robust and sensitive pipeline and confirmed the significant differential expression of hsa-miR-16-5p, hsa-miR-19b-3p, hsa-miR-92a-2-5p, hsa-miR-128-3p (ranked first), hsa-miR-130b-3p and -5p, hsa-miR-181a-5p, -2-3p and -3p, hsa-miR-181b-5p and -3p, hsa-miR-145-5p and hsa-miR-574-3p, as described in the literature, along with novel identified miRNAs.

It has been reported that there were several \"mutant isolated in the field \" of African swine fever virus (ASFV) since ASFV was reported, which may be the result of the continuous adaptation and evolution of ASFV. The emergence of ASFV field mutants may lead to chronic or asymptomatic \"atypical clinical symptoms\" in pigs and hinder the development of porcine industry. Here we analyzed the published ASFV \"field attenuated strain\" gene sequences and reviewed the genetic differences between field attenuated and virulent ASFV strains, hoping for providing a reference for the scientific prevention and control of ASF and the development of new vaccines. In this study we found the deletion of EP153R and EP402R occurred in 4 field attenuated strains, and all the differential genes of field attenuated strains mainly range in regions with low GC content. The evolution of MGF110 family genes was identified by analysis of two field attenuated ASFV strains from Portugal. We also found that some tandem repeat sequence plays an important role in the evolution of strains of NH/P68 and OURT 88/3 but not in strains Estonia 2014, HuB20 and Pig/Heilongjiang/HRB1/2020.

Lysine succinylation is a post-translational modification (PTM) of protein in which a succinyl group (-CO-CH2-CH2-CO2H) is added to a lysine residue of protein that reverses lysine\'s positive charge to a negative charge and leads to the significant changes in protein structure and function. It occurs on a wide range of proteins and plays an important role in various cellular and biological processes in both eukaryotes and prokaryotes. Beyond experimentally identified succinylation sites, there have been a lot of studies for developing sequence-based prediction using machine learning approaches, because it has the promise of being extremely time-saving, accurate, robust, and cost-effective. Despite these benefits for computational prediction of lysine succinylation sites for different species, there are a number of issues that need to be addressed in the design and development of succinylation site predictors. In spite of the fact that many studies used different statistical and machine learning computational tools, only a few studies have focused on these bioinformatics issues in depth. Therefore, in this comprehensive comparative review, an attempt is made to present the latest advances in the prediction models, datasets, and online resources, as well as the obstacles and limits, to provide an advantageous guideline for developing more suitable and effective succinylation site prediction tools.