MultiTrans: An Algorithm for Path Extraction Through Mixed Integer Linear Programming for Transcriptome Assembly

Cov-trans: an efficient algorithm for discontinuous transcript assembly in coronaviruses

BACKGROUND

Discontinuous transcription allows coronaviruses to efficiently replicate and transmit within host cells, enhancing their adaptability and survival. Assembling viral transcripts is crucial for virology research and the development of antiviral strategies. However, traditional transcript assembly methods primarily designed for variable alternative splicing events in eukaryotes are not suitable for the viral transcript assembly problem. The current algorithms designed for assembling viral transcripts often struggle with low accuracy in determining the transcript boundaries. There is an urgent need to develop a highly accurate viral transcript assembly algorithm.

RESULTS

In this work, we propose Cov-trans, a reference-based transcript assembler specifically tailored for the discontinuous transcription of coronaviruses. Cov-trans first identifies canonical transcripts based on discontinuous transcription mechanisms, start and stop codons, as well as reads alignment information. Subsequently, it formulates the assembly of non-canonical transcripts as a path extraction problem, and introduces a mixed integer linear programming to recover these non-canonical transcripts.

CONCLUSION

Experimental results show that Cov-trans outperforms other assemblers in both accuracy and recall, with a notable strength in accurately identifying the boundaries of transcripts. Cov-trans is freely available at https://github.com/computer-Bioinfo/Cov-trans.git .

A split Bregman method solving optimal reactive power dispatch for a doubly-fed induction generator-based wind farm

This paper proposes an optimal reactive power control method to maximize wind farm revenue and minimize the total electrical losses of a doubly-fed induction generator (DFIG)-based wind farm. Specifically, the split Bregman method is used to solve the optimal control problem in a distributed manner. That is, the optimization problem is decomposed into sub-problems by the optimal distributed control strategy, and each sub-problem is solved independently in each local controller through the parallel method, which reduces the calculating burden and improves the information privacy. Thus, when a fault occurs, the proposed distributed control strategy can overcome the system fault and improve the reliability and security of the system. Furthermore, an economic financial model of annual revenue is contributed to examine the income impact with or without certified emission reduction (CER) by the clean development mechanism (CDM). Compared with the dual ascent (DA) method, sequential quadratic programming (SQP) method and the proportional dispatch method (PDM), the annual revenue (AR) of the wind farm using the proposed split Bregman method is the highest. Simulation results demonstrate that this method has promising performance in both optimization quality and computational efficiency.

Jumper enables discontinuous transcript assembly in coronaviruses

Genes in SARS-CoV-2 and other viruses in the order of Nidovirales are expressed by a process of discontinuous transcription which is distinct from alternative splicing in eukaryotes and is mediated by the viral RNA-dependent RNA polymerase. Here, we introduce the DISCONTINUOUS TRANSCRIPT ASSEMBLYproblem of finding transcripts and their abundances given an alignment of paired-end short reads under a maximum likelihood model that accounts for varying transcript lengths. We show, using simulations, that our method, JUMPER, outperforms existing methods for classical transcript assembly. On short-read data of SARS-CoV-1, SARS-CoV-2 and MERS-CoV samples, we find that JUMPER not only identifies canonical transcripts that are part of the reference transcriptome, but also predicts expression of non-canonical transcripts that are supported by subsequent orthogonal analyses. Moreover, application of JUMPER on samples with and without treatment reveals viral drug response at the transcript level. As such, JUMPER enables detailed analyses of Nidovirales transcriptomes under varying conditions.