Gene fusion is a prevalent occurrence in cancer patients, and fusions are significant both as diagnostic biomarkers and as therapeutic targets for cancer. Long-read transcriptome sequencing technology provides new opportunities for gene fusion detection. In this research, we have developed GFvoter, a novel method that employs a multivoting strategy to identify gene fusions from long-read transcriptome sequencing data. GFvoter calls two RNA-seq aligners, two fusion detection tools, and a newly designed scoring mechanism to conduct the so-called voting process in turn, which enables the accurate detection of potential fusions. We validated GFvoter using both simulated and real cell line datasets from PacBio and Nanopore and found that GFvoter significantly outperforms alternative methods. Moreover, GFvoter successfully reported the RPS6KB1:VMP1 gene fusion in the MCF-7 cell line, while none of the other tested tools detected this fusion. Overall, our findings show that GFvoter can accurately identify gene fusions from long-read RNA-seq data, which has the potential to improve cancer diagnosis and treatment. GFvoter is available at https://github.com/xiaolan-z/GFvoter .

Vector form expression of logical (control) networks is presented. From this aspect, the trajectory table is proposed to investigate Boolean networks. Based on it, the topology structure, controllability and observability of logical (control) networks are analyzed. Compared to the method of logical matrix, vector form expression called structure vector method decreases the computational complex. Numerical examples show that the complexity of the structure vector method is greatly reduced.

With an increase in tracking time, the operating cost of the controller will increase accordingly. Considering the biological applications of Boolean control networks (BCNs), it is necessary to study the control problem of BCNs over finite time. In this paper, we study the output tracking problem of a BCN with disturbance inputs in a given finite time. First, the logical form of BCNs is transformed into an algebraic form using the semi-tensor product (STP) method. Then, the robust output tracking problems of a reference output trajectory and the outputs of a reference system over finite time are transformed into the robust reachability problem of the BCNs. Based on the truth matrix technique, two necessary and sufficient conditions are provided for the trackability of the reference outputs over finite time. Moreover, two algorithms are proposed to design the controllers in the case of the traceable outputs. It should be pointed out that the truth matrix method we used here has some unique features, including its simple computation and concise expression. Finally, two illustrative examples are presented to demonstrate the results in this paper.

Full-length transcript reconstruction has a pivotal role in RNA-seq data analysis. In this research, we present a new genome-guided transcriptome assembly algorithm, namely Tiglon, which integrates multiple alignments of different mapping tools and builds the labeled splice graphs, followed by a label-based dynamic path-searching strategy to reconstruct the transcripts. We evaluate Tiglon on a simulated dataset and 12 real datasets under the Hisat2 and Star mappings. The results indicate that the integrating techniques of Tiglon exhibit great superiority over the state-of-the-art assemblers, including StringTie2 and Scallop, depending on Hisat2 alignments, Star alignments, or the merged alignments of both. Especially, Tiglon is significantly powerful in recovering lowly expressed transcripts.