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Metagenomic Hi-C (metaHi-C) has shown remarkable potential for retrieving high-quality metagenome-assembled genomes from complex microbial communities. Nevertheless, existing metaHi-C-based contig binning methods solely rely on Hi-C interactions between contigs, disregarding crucial biological information such as the presence of single-copy marker genes. To overcome this limitation, we introduce ImputeCC, an integrative contig binning tool optimized for metaHi-C datasets. ImputeCC integrates both Hi-C interactions and the discriminative power of single-copy marker genes to group marker-gene-containing contigs into preliminary bins. It also introduces a novel constrained random walk with restart algorithm to enhance Hi-C connectivity among contigs. Comprehensive assessments using both mock and real metaHi-C datasets from diverse environments demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. A genus-level analysis of the sheep gut microbiota reconstructed by ImputeCC underlines its capability to recover key species from dominant genera and identify previously unknown genera.

The association of colorectal cancer (CRC) and the human gut microbiome dysbiosis has been the focus of several studies in the past. Many bacterial taxa have been shown to have differential abundance among CRC patients compared to healthy controls. However, the relationship between CRC and non-bacterial gut microbiome such as the gut virome is under-studied and not well understood. In this study we conducted a comprehensive analysis of the association of viral abundances with CRC using metagenomic shotgun sequencing data of 462 CRC subjects and 449 healthy controls from 7 studies performed in 8 different countries. Despite the high heterogeneity, our results showed that the virome alpha diversity was consistently higher in CRC patients than in healthy controls (p-value <0.001). This finding is in sharp contrast to previous reports of low alpha diversity of prokaryotes in CRC compared to healthy controls. In addition to the previously known association of Podoviridae , Siphoviridae and Myoviridae with CRC, we further demonstrate that Herelleviridae , a newly constructed viral family, is significantly depleted in CRC subjects. Our interkingdom association analysis reveals a less intertwined correlation between the gut virome and bacteriome in CRC compared to healthy controls. Furthermore, we show that the viral abundance profiles can be used to accurately predict CRC disease status (AUROC >0.8) in both within-study and cross-study settings. The combination of training sets resulted in rather generalized and accurate prediction models. Our study clearly shows that subjects with colorectal cancer harbor a distinct human gut virome profile which may have an important role in this disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zuo, Michail and Sun.)

Dysbiosis of human gut microbiota has been reported in association with ulcerative colitis (UC) in both children and adults using either 16S rRNA gene or shotgun sequencing data. However, these studies used either 16S rRNA or metagenomic shotgun sequencing but not both. We sequenced feces samples from 19 pediatric UC and 23 healthy children ages between 7 to 21 years using both 16S rRNA and metagenomic shotgun sequencing. The samples were analyzed using three different types of data: 16S rRNA genus level abundance, microbial species and pathway abundance profiles. We demonstrated that (a) the alpha diversity of pediatric UC cases is lower than that of healthy controls; (b) the beta diversity within children with UC is more variable than within the healthy children; (c) several microbial families including Akkermansiaceae, Clostridiaceae, Eggerthellaceae, Lachnospiraceae, and Oscillospiraceae, contain species that are depleted in pediatric UC compared to controls; (d) a few associated species unique to pediatric UC, but not adult UC, were also identified, e.g. some species in the Christensenellaceae family were found to be depleted and some species in the Enterobacteriaceae family were found to be enriched in pediatric UC; and (e) both 16S rRNA and shotgun sequencing data can predict pediatric UC status with area under the receiver operating characteristic curve (AUROC) of close to 0.90 based on cross validation. We showed that 16S rRNA data yielded similar results as shotgun data in terms of alpha diversity, beta diversity, and prediction accuracy. Our study demonstrated that pediatric UC subjects harbor a dysbiotic and less diverse gut microbial population with distinct differences from healthy children. We also showed that 16S rRNA data yielded accurate disease prediction results in comparison to shotgun data, which can be more expensive and laborious. These conclusions were confirmed in an independent data set of 7 pediatric UC cases and 8 controls., (© 2022. The Author(s).)

Grant support (GS) in the MEDLINE database refers to funding agencies and contract numbers. It is important for funding organizations to track their funding outcomes from the GS information. As such, how to accurately and automatically extract funding information from biomedical literature is challenging. In this paper, we present a pipeline system called GrantExtractor that is able to accurately extract GS information from fulltext biomedical literature. GrantExtractor effectively integrates several advanced machine learning techniques. In particular, we use a sentence classifier to identify funding sentences from articles first. A bi-directional LSTM and the CRF layer (BiLSTM-CRF), and pattern matching are then used to extract entities of grant numbers and agencies from these identified funding sentences. After removing noisy numbers by a multi-class model, we finally match each grant number with its corresponding agency. Experimental results on benchmark datasets have demonstrated that GrantExtractor clearly outperforms all baseline methods. It is further evident that GrantExtractor won the first place in Task 5C of 2017 BioASQ challenge, with achieving the Micro-recall of 0.9526 for 22,610 articles. Moreover, GrantExtractor has achieved the Micro F-measure score as high as 0.90 in extracting grant pairs.