Winfree loop sustained oscillation in two-dimensional excitable lattices: Prediction and realization

New Burst-Oscillation Mode in Paced One-Dimensional Excitable Systems

A new type of burst-oscillation mode (BOM) is reported for the first time, by extensively investigating the response dynamics of a one-dimensional (1D) paced excitable system with unidirectional coupling. The BOM state is an alternating transition between two distinct phases, i.e., the phase with multiple short spikes and the phase with a long interval. The realizable region and the unrealizable region for the evolution of BOM are identified, which is determined by the initial pulse number in the system. It is revealed that, in the realizable region, the initial inhomogeneous BOM will eventually evolve to the homogeneously distributed spike-oscillation mode (SOM), while it can maintain in the unrealizable region. Furthermore, several dynamical features of BOM and SOM are theoretically predicted and have been verified in numerical simulations. The mechanisms of the emergence of BOM are discussed in detail. It is revealed that three key factors, i.e., the linking time, the system length, and the local dynamics, can effectively modulate the pattern of BOM. Moreover, the suitable parameter region of the external pacing (A, f) that can produce the new type of BOM, has been explicitly revealed. These results may facilitate a deeper understanding of bursts in nature and will have a useful impact in related fields.

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

With the advent of next-generation sequencing technology, it has become convenient and cost efficient to thoroughly characterize the microbial diversity and taxonomic composition in various environmental samples. Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists. One particular challenge is the OTUs (operational taxonomic units) picking in 16S rRNA sequence analysis. Lucky, this challenge can be directly addressed by sequence clustering that attempts to group similar sequences. Therefore, numerous clustering methods have been proposed to help to cluster 16S rRNA sequences into OTUs. However, each method has its clustering mechanism, and different methods produce diverse outputs. Even a slight parameter change for the same method can also generate distinct results, and how to choose an appropriate method has become a challenge for inexperienced users. A lot of time and resources can be wasted in selecting clustering tools and analyzing the clustering results. In this study, we introduced the recent advance of clustering methods for OTUs picking, which mainly focus on three aspects: (i) the principles of existing clustering algorithms, (ii) benchmark dataset construction for OTU picking and evaluation metrics, and (iii) the performance of different methods with various distance thresholds on benchmark datasets. This paper aims to assist biological researchers to select the reasonable clustering methods for analyzing their collected sequences and help algorithm developers to design more efficient sequences clustering methods.

smsMap: mapping single molecule sequencing reads by locating the alignment starting positions

Background

Single Molecule Sequencing (SMS) technology can produce longer reads with higher sequencing error rate. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. Most existing mapping tools generally adopt the traditional seed-and-extend strategy, and the candidate aligned regions for each query read are selected either by counting the number of matched seeds or chaining a group of seeds. However, for all the existing mapping tools, the coverage ratio of the alignment region to the query read is lower, and the read alignment quality and efficiency need to be improved. Here, we introduce smsMap, a novel mapping tool that is specifically designed to map the long reads of SMS to a reference genome.

Results

smsMap was evaluated with other existing seven SMS mapping tools (e.g., BLASR, minimap2, and BWA-MEM) on both simulated and real-life SMS datasets. The experimental results show that smsMap can efficiently achieve higher aligned read coverage ratio and has higher sensitivity that can align more sequences and bases to the reference genome. Additionally, smsMap is more robust to sequencing errors.

Conclusions

smsMap is computationally efficient to align SMS reads, especially for the larger size of the reference genome (e.g., H. sapiens genome with over 3 billion base pairs). The source code of smsMap can be freely downloaded from https://github.com/NWPU-903PR/smsMap.