Btrim: A fast, lightweight adapter and quality trimming program for next-generation sequencing technologies (1410.6455v1)

Abstract: Btrim is a fast and lightweight software to trim adapters and low quality regions in reads from ultra high-throughput next-generation sequencing machines. It also can reliably identify barcodes and assign the reads to the original samples. Based on a modified Myers's bit-vector dynamic programming algorithm, Btrim can handle indels in adapters and barcodes. It removes low quality regions and trims off adapters at both or either end of the reads. A typical trimming of 30M reads with two sets of adapter pairs can be done in about a minute with a small memory footprint. Btrim is a versatile stand-alone tool that can be used as the first step in virtually all next-generation sequence analysis pipelines. The program is available at \url{http://graphics.med.yale.edu/trim/}.

• The paper presents Btrim, a tool that enhances NGS preprocessing by using a modified Myers’s bit-vector algorithm for efficient adapter trimming and indel correction.
• It integrates a moving window quality trimming method to remove low-quality regions, ensuring high-quality reads for subsequent analyses.
• Btrim operates with minimal memory and rapid processing, trimming millions of reads in under a minute, making it ideal for large-scale sequencing projects.

Overview of Btrim: A Fast, Lightweight Adapter and Quality Trimming Program for Next-Generation Sequencing Technologies

The paper presents Btrim, a software solution designed to address the challenges associated with processing data from next-generation sequencing (NGS) technologies. Btrim focuses on efficient adapter trimming and quality control, crucial steps in preparing sequencing data for downstream analyses, such as mapping and assembly.

Core Features and Methodology

Btrim leverages a modified Myers's bit-vector dynamic programming algorithm. This allows it to efficiently handle insertions and deletions (indels) within adapters and barcodes. The program is capable of removing low-quality regions and trimming adapters from both or one end of the reads, making it a versatile tool for diverse sequencing projects.

Algorithmic Approach:

• The Myers's algorithm is recognized for its efficiency, operating with a complexity linear to the target sequence length $n$, irrespective of the error threshold $k$ or the query length.
• The algorithm processes query sequences beforehand, ensuring negligible setup time relative to the volume of target sequences encountered.
• Modifications to the original algorithm enable reverse searching, which is critical in identifying the starting positions of queries in target sequences, especially useful for 3’ adapter trimming.

The default parameters set $k$ to 3 and 4 for 5'- and 3'-adapter searches, respectively, adaptable based on user input or adapter length. Btrim also implements a moving window algorithm for quality trimming, determining the trim point where the average quality score falls below a specified threshold.

Performance and Capabilities

Btrim is structured to function with minimal computational resource demand. For instance, trimming 30 million 75bp Illumina reads with two adapter pairs is achieved in approximately one minute on a 3.16GHz Intel Xeon processor, while maintaining a memory footprint of less than 1MB. The program currently supports the FASTQ format, covering both Sanger and Illumina specifications.

Implications and Future Directions

Btrim’s lightweight and efficient nature allows it to be integrated as a preliminary step in various NGS pipelines, ensuring that the datasets used are free from artifacts that could skew results. Its ability to handle sequences with errors in barcodes and adapters adds robustness to experiments using multiplexed samples.

The implications of Btrim extend to improving the accuracy of genomic mappings and assemblies, thereby reducing false positives in variant analysis. As NGS technologies continue to scale, tools like Btrim will be instrumental in managing the increased data volumes and complexities.

Future Prospects:

• Enhanced versions could incorporate support for additional sequence formats and more sophisticated error models.
• Scaling to accommodate even longer read technologies and more complex datasets will be valuable.
• Integration with cloud computing frameworks could facilitate processing of large-scale datasets in distributed environments.

In conclusion, Btrim offers a significant contribution to the toolkit available for genomic data preprocessing, presenting a reliable, efficient, and flexible option for researchers in the field of next-generation sequencing.