Single cell transcriptomic profiling of a neuron-astrocyte assembloid tauopathy model.

The use of iPSC derived brain organoid models to study neurodegenerative disease has been hampered by a lack of systems that accurately and expeditiously recapitulate pathogenesis in the context of neuron-glial interactions. Here we report development of a system, termed AstTau, which propagates toxic human tau oligomers in iPSC derived neuron-astrocyte assembloids. The AstTau system develops much of the neuronal and astrocytic pathology observed in tauopathies including misfolded, phosphorylated, oligomeric, and fibrillar tau, strong neurodegeneration, and reactive astrogliosis.

Transcriptome Profiling of ' Liberibacter asiaticus' in Citrus and Psyllids.

' Liberibacter asiaticus' (Las) is an emergent bacterial pathogen that is associated with the devastating citrus huanglongbing (HLB). Vectored by the Asian citrus psyllid, Las colonizes the phloem tissue of citrus, causing severe damage to infected trees. So far, cultivating pure Las culture in axenic media has not been successful, and dual-transcriptome analyses aiming to profile gene expression in both Las and its hosts have a low coverage of the Las genome because of the low abundance of bacterial RNA in total RNA extracts from infected tissues.

FINER: enhancing the prediction of tissue-specific functions of isoforms by refining isoform interaction networks.

Annotating the functions of gene products is a mainstay in biology. A variety of databases have been established to record functional knowledge at the gene level. However, functional annotations at the isoform resolution are in great demand in many biological applications.

DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.

Background: Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computational methods have been proposed. Although these computational methods have achieved promising prediction performance, they neglect the fact that a gene may encode multiple protein isoforms and different isoforms of the same gene may interact differently with the same lncRNA.

DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning.

Motivation: Alternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gene functions have been well studied, little is known about the specific functions of isoforms, making accurate prediction of isoform functions highly desirable. However, the existing approaches to predicting isoform functions are far from satisfactory due to at least two reasons: (i) unlike genes, isoform-level functional annotations are scarce.

DeepIsoFun: a deep domain adaptation approach to predict isoform functions.

Motivation: Isoforms are mRNAs produced from the same gene locus by alternative splicing and may have different functions. Although gene functions have been studied extensively, little is known about the specific functions of isoforms. Recently, some computational approaches based on multiple instance learning have been proposed to predict isoform functions from annotated gene functions and expression data, but their performance is far from being desirable primarily due to the lack of labeled training data.

Protein folding in HP model on hexagonal lattices with diagonals.

Three dimensional structure prediction of a protein from its amino acid sequence, known as protein folding, is one of the most studied computational problem in bioinformatics and computational biology. Since, this is a hard problem, a number of simplified models have been proposed in literature to capture the essential properties of this problem. In this paper we introduce the hexagonal lattices with diagonals to handle the protein folding problem considering the well researched HP model.