N6-Methyladenosine (m6A) is the most abundant type of mRNA methylation and is most widely measured by methylated RNA immunoprecipitation sequencing (MeRIP-seq). In MeRIP-seq, an immunoprecipitation (IP) sample and a pairing control (input) sample are sequenced for each biological sample. Methylated regions are identified as peaks showing increased counts in the IP sample versus the input. We report that technical bias in sequencing can vary substantially in the IP and input samples depending on the local sequence context. Current sequencing depth-based normalization does not appropriately account for the varying technical bias along the transcriptome and leads to inaccurate identification of m6A regions. We describe a method to estimate a local size factor that reflects the RNA sequence context and show that peak calling using these region-specific size factors identifies more accurate peak regions.

This paper presents a cognition-based transmitter polarization optimization method for the conformal array space-time adaptive processing. First, we introduce the signal model and working mechanism of the airborne conformal array radar. Then the polarization characteristics of the target are recognized through the least squares criterion. Leveraging the estimated target scattering matrix, we further design the transmitter polarization optimization problem. Finally, the semidefinite relaxation approach is adopted to obtain the optimized transmit weight vector. Simulation results demonstrate the effectiveness of the proposed method in clutter cancellation.

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

Lysine 2-hydroxyisobutylation (Khib), which was first reported in 2014, has been shown to play vital roles in a myriad of biological processes including gene transcription, regulation of chromatin functions, purine metabolism, pentose phosphate pathway and glycolysis/gluconeogenesis. Identification of Khib sites in protein substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein 2-hydroxyisobutylation. Experimental identification of Khib sites mainly depends on the combination of liquid chromatography and mass spectrometry. However, experimental approaches for identifying Khib sites are often time-consuming and expensive compared with computational approaches. Previous studies have shown that Khib sites may have distinct characteristics for different cell types of the same species. Several tools have been developed to identify Khib sites, which exhibit high diversity in their algorithms, encoding schemes and feature selection techniques. However, to date, there are no tools designed for predicting cell type-specific Khib sites. Therefore, it is highly desirable to develop an effective predictor for cell type-specific Khib site prediction. Inspired by the residual connection of ResNet, we develop a deep learning-based approach, termed ResNetKhib, which leverages both the one-dimensional convolution and transfer learning to enable and improve the prediction of cell type-specific 2-hydroxyisobutylation sites. ResNetKhib is capable of predicting Khib sites for four human cell types, mouse liver cell and three rice cell types. Its performance is benchmarked against the commonly used random forest (RF) predictor on both 10-fold cross-validation and independent tests. The results show that ResNetKhib achieves the area under the receiver operating characteristic curve values ranging from 0.807 to 0.901, depending on the cell type and species, which performs better than RF-based predictors and other currently available Khib site prediction tools. We also implement an online web server of the proposed ResNetKhib algorithm together with all the curated datasets and trained model for the wider research community to use, which is publicly accessible at https://resnetkhib.erc.monash.edu/.

Modern high throughput technologies routinely produce a large set of genomic loci of biological interest like genome-wide association studies (GWASs). Annotating the set of genomic loci may lead to new biological insights. However, available tools are limited.In this study, we developed a new bioinformatics software package named loci2tissue that aims to connect a set of input genomic loci to tissues and organs, thus providing annotation in terms of tissue-specific transcription regulation potential. This is achieved by utilizing multi-tissue expression quantitative trait loci (eQTLs) information provided by Genotype-Tissue Expression (GTEx) to connect genomic loci to genes in a tissue-specific manner. We then rank tissues based on the tissue-specific expression levels of these genes. When applying loci2tissue to sets of loci that harbor genetic variants linked to complex diseases, we are able to identify specific tissues involved in complex diseases or traits.Our analyses revealed interesting tissue-trait pairs. As examples, we found significant enrichment of visceral omental adipose tissue in Alzheimer’s disease and the hippocampus tissue in Parkinson’s disease. These results shed light on the underlying biology of many complex diseases and traits where the tissue is likely to be the source of pathogenesis.

Over the past decade, Genome-wide association studies (GWAS) have been successfully developed and applied to identify many sequence variants that are significantly associated with common diseases and traits. Hundreds of thousands of such trait-associated variants have already been cataloged, making GWASs great resources for complex disease research. The main challenge ahead lies in elucidating disease mechanisms from these findings. In this study, we conducted a systematic survey of the relationships between genes which have been linked to complex diseases and previously researched genetic associations to explore whether any enrichment of known biological knowledge exists. To achieve this, we queried these genes against various collections of previously researched genomic associations through Enrichr. We uncovered many intriguing enrichment patterns between disease-associated genes and biomedical conditions such as the association between Obesity & Hot Drink Temperature, Alzheimer's and Nose Size, and Body Mass Index & the PCDHA10 Pathway. These findings could potentially shed light on the pathogenicity of these complex diseases and may help in the future development of novel therapeutic treatments.