Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment

Over the past decades, massive amounts of protein-protein interaction (PPI) data have been accumulated due to the advancement of high-throughput technologies, and but data quality issues (noise or incompleteness) of PPI have been still affecting protein function prediction accuracy based on PPI networks. Although two main strategies of network reconstruction and edge enrichment have been reported on the effectiveness of boosting the prediction performance in numerous literature studies, there still lack comparative studies of the performance differences between network reconstruction and edge enrichment. Inspired by the question, this study first uses three protein similarity metrics (local, global and sequence) for network reconstruction and edge enrichment in PPI networks, and then evaluates the performance differences of network reconstruction, edge enrichment and the original networks on two real PPI datasets. The experimental results demonstrate that edge enrichment work better than both network reconstruction and original networks. Moreover, for the edge enrichment of PPI networks, the sequence similarity outperformes both local and global similarity. In summary, our study can help biologists select suitable pre-processing schemes and achieve better protein function prediction for PPI networks.

Vertical transmission of tissue microbiota in Caenorhabditis elegans

The past forty-five years has witnessed Caenorhabditis elegans as the most significant model animal in life science since its discovery seventy years ago1,2, as it introduced principles of gene regulated organ development, and RNA interference into biology3-5. Meanwhile, it has become one of the lab animals in gut microbiota studies as these symbionts contribute significantly to many aspects in host biology6,7. Meanwhile, the origin of gut microbiota remains debatable in human8- 11, and has not been investigated in other model animals. Here we show that the symbiont bacteria in C. elegans not only vertically transmit from the parent generation to the next, but also distributes in the worm tissues parallel with its development. We found that bacteria can enter into the embryos of C. elegans, a step associated with vitellogenin, and passed to the next generation. These vertically transmitted bacteria share global similarity, and bacterial distribution in worm tissues changes as they grow at different life stages. Antibiotic treatment of worms increased their vulnerability against pathogenic bacteria, and replenishment of tissue microbiota restored their immunity. These results not only offered a molecular basis of vertical transmission of bacteria in C. elegans, but also signal a new era for the mixed tissue cell-bacteria multi-species organism study.

Global similarity, and some key differences, in the metagenomes of Swedish varroa-surviving and varroa-susceptible honeybees

There is increasing evidence that honeybees (Apis mellifera L.) can adapt naturally to survive Varroa destructor, the primary cause of colony mortality world-wide. Most of the adaptive traits of naturally varroa-surviving honeybees concern varroa reproduction. Here we investigate whether factors in the honeybee metagenome also contribute to this survival. The quantitative and qualitative composition of the bacterial and viral metagenome fluctuated greatly during the active season, but with little overall difference between varroa-surviving and varroa-susceptible colonies. The main exceptions were Bartonella apis and sacbrood virus, particularly during early spring and autumn. Bombella apis was also strongly associated with early and late season, though equally for all colonies. All three affect colony protein management and metabolism. Lake Sinai virus was more abundant in varroa-surviving colonies during the summer. Lake Sinai virus and deformed wing virus also showed a tendency towards seasonal genetic change, but without any distinction between varroa-surviving and varroa-susceptible colonies. Whether the changes in these taxa contribute to survival or reflect demographic differences between the colonies (or both) remains unclear.

A Hybrid Retrieval Strategy for Case-based Reasoning Using Soft Likelihood Functions

Case-based reasoning (CBR) is the retrieval of one or more similar cases from an existing case base for the problem to be solved according to the characteristics of the new problem. The core idea of CBR is that similar cases have similar solutions, so whether the CBR system can play a powerful advantage depends on the quality of case retrieval strategy. At present, the commonly used case retrieval algorithm is based on the mean operator method, which is very hard, and a certain local similarity is low will affect the overall result. In order to calculate the global similarity of cases from a new and softer point of view, this paper introduces the soft likelihood functions into case retrieval, combines the soft likelihood functions with KNN, and proposes a hybrid retrieval strategy. The core of the retrieval strategy is to define the global similarity through SLFs, aggregate the local similarity and characteristic similarity together, and also take the attitude characteristics of decision makers into consideration. Through simulation experiments on real data sets, the accuracy rate is more than 81%, which verifies the effectiveness of the retrieval strategy.

Clustering single-cell RNA-seq data by rank constrained similarity learning

MotivationRecent breakthroughs of single-cell RNA sequencing (scRNA-seq) technologies offer an exciting opportunity to identify heterogeneous cell types in complex tissues. However, the unavoidable biological noise and technical artifacts in scRNA-seq data as well as the high dimensionality of expression vectors make the problem highly challenging. Consequently, although numerous tools have been developed, their accuracy remains to be improved.ResultsHere, we introduce a novel clustering algorithm and tool RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similarity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. RCSL automatically estimates the number of cell types defined in the similarity matrix, and identifies them by constructing a block-diagonal matrix, such that its distance to the similarity matrix is minimized. Each block-diagonal submatrix is a cell cluster/type, corresponding to a connected component in the cognate similarity graph. When tested on 16 benchmark scRNA-seq datasets in which the cell types are well-annotated, RCSL substantially outperformed six state-of-the-art methods in accuracy and robustness as measured by three metrics.AvailabilityThe RCSL algorithm is implemented in R and can be freely downloaded at https://github.com/QinglinMei/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data

It is a common problem facing recommender to sparse data dealing, especially for crowdfunding recommendations. The collaborative filtering (CF) tends to recommend a user those items only connecting to similar users directly but fails to recommend the items with indirect actions to similar users. Therefore, CF performs poorly in the case of sparse data like Kickstarter. We propose a method of enabling indirect crowdfunding campaign recommendation based on bipartite graph. PersonalRank is applicable to calculate global similarity; as opposed to local similarity, for any node of the network, we use PersonalRank in an iterative manner to produce recommendation list where CF is invalid. Furthermore, we propose a bipartite graph-based CF model by combining CF and PersonalRank. The new model classifies nodes into one of the following two types: user nodes and campaign nodes. For any two types of nodes, the global similarity between them is calculated by PersonalRank. Finally, a recommendation list is generated for any node through CF algorithm. Experimental results show that the bipartite graph-based CF achieves better performance in recommendation for the extremely sparse data from crowdfunding campaigns.