ABSTRACT
Background: The increasingly widespread use of next generation sequencing protocols has brought the need for the development of user-friendly raw data processing tools. Here, we explore 2FAST2Q, a versatile and intuitive standalone program capable of extracting and counting feature occurrences in FASTQ files. Despite 2FAST2Q being previously described as part of a CRISPRi-seq analysis pipeline, in here we further elaborate on the program's functionality, and its broader applicability and functions. Methods: 2FAST2Q is built in Python, with published standalone executables in Windows MS, MacOS, and Linux. It has a familiar user interface, and uses an advanced custom sequence searching algorithm. Results: Using published CRISPRi datasets in which Escherichia coli and Mycobacterium tuberculosis gene essentiality, as well as host-cell sensitivity towards SARS-CoV2 infectivity were tested, we demonstrate that 2FAST2Q efficiently recapitulates published output in read counts per provided feature. We further show that 2FAST2Q can be used in any experimental setup that requires feature extraction from raw reads, being able to quickly handle Hamming distance based mismatch alignments, nucleotide wise Phred score filtering, custom read trimming, and sequence searching within a single program. Moreover, we exemplify how different FASTQ read filtering parameters impact downstream analysis, and suggest a default usage protocol. 2FAST2Q is easier to use and faster than currently available tools, efficiently processing not only CRISPRi-seq / random-barcode sequencing datasets on any up-to-date laptop, but also handling the advanced extraction of de novo features from FASTQ files. We expect that 2FAST2Q will not only be useful for people working in microbiology but also for other fields in which amplicon sequencing data is generated. 2FAST2Q is available as an executable file for all current operating systems without installation and as a Python3 module on the PyPI repository (available at https://veeninglab.com/2fast2q).
ABSTRACT
Therapeutic advances in the 20th century significantly reduced tuberculosis (TB) mortality. Nonetheless, TB still poses a massive global health challenge with significant annual morbidity and mortality that has been amplified during the COVID-19 pandemic. Unlike most common bacterial infectious diseases, successful TB treatment requires months-long regimens, which complicates the ability to treat all cases quickly and effectively. Improving TB chemotherapy by reducing treatment duration and optimizing combinations of drugs is an important step to reducing relapse. In this review, we outline the limitations of current multidrug regimens against TB and have reviewed the genetic tools available to improve the identification of drug targets. The rational design of regimens that sterilize diverse phenotypic subpopulations will maximize bacterial killing while minimizing both treatment duration and infection relapse. Importantly, the TB field currently has all the necessary genetic and analytical tools to screen for and prioritize drug targets in vitro based on the vulnerability of essential and non-essential genes in the Mtb genome and to translate these findings in in vivo models. Combining genetic methods with chemical screens offers a formidable strategy to redefine the preclinical design of TB therapy by identifying powerful new targets altogether, as well as targets that lend new efficacy to existing drugs.