AfterQC: automatic filtering, trimming, error removing and quality control for fastq data

Chen, Shifu; Huang, Tanxiao; Zhou, Yanqing; Han, Yue; Xu, Mingyan; Gu, Jia

doi:10.1186/s12859-017-1469-3

Cited by 305 publications

(220 citation statements)

References 17 publications

(16 reference statements)

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“…The generated DNA sequencing fastq files were processed with the breseq computational pipeline (version 0.32.0) 33 and aligned to an E. coli K12 MG1655 reference genome 34 to identify mutations. DNA-seq quality control was accomplished using the software AfterQC (version 0.9.7) 35 . Clones were chosen in order to represent the high-frequency alleles found in the end-point populations of the respective ALE experiments.…”

Section: Strain Genomic Sequencingmentioning

confidence: 99%

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Heckmann

Campeau

Lloyd

et al. 2019

Preprint

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Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, nonphysiological, inconsistent, and labor-intensive to obtain. We use a data-driven approach to estimate in vivo kcats using metabolic specialist E. coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of noisy and inconsistent parameterizations of cellular models.

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Section: Strain Genomic Sequencingmentioning

confidence: 99%

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Heckmann

Campeau

Lloyd

et al. 2019

Preprint

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“…uk/projects/fastqc). Removal of adapters were performed using AfterQC (28). Reads were aligned to the mouse reference genome (GRCm38.p5 Release M15) using STAR aligner (29).…”

Section: Analysis Of Mrna-seq Librarymentioning

confidence: 99%

Calorie restriction reprograms diurnal rhythms in protein translation to regulate metabolism

et al. 2018

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Calorie restriction (CR) delays aging and affects the circadian clocks by reprogramming circadian rhythms in gene expression. To expand on the circadian mechanisms in CR, we assayed rhythms in the protein translation by analyzing polysome‐associated mRNAs in the liver of mice fed ad libitum (AL) and CR diets. Global comparison of the diets revealed that <1% of transcripts were differentially abundant in the polysomes. In contrast, the large differential, up to 10%, was detected when CR and AL diets were compared at individual times throughout the day. Most transcripts that were rhythmic under AL lost their rhythms, and many new transcripts gained rhythms under CR. Only a small fraction of transcripts, including the circadian clock genes, were rhythmic under both diets. Thus, CR strongly reprograms translation. CR affected translation of enzymes regulating long‐chain acetyl‐coenzyme A (Acyl‐CoA) metabolism. The expression of the Acyl‐CoA thioesterase (ACOT) family was induced upon CR, leading to the increased transcriptional activity of peroxisome proliferator‐activated receptor α, the transcriptional factor regulated by the ACOT products. We propose that the differential translation induced by CR leads to a temporal partition and reprogramming of metabolic processes and provides a link between CR, lipid metabolism, and the circadian clock.—Makwana, K., Gosai, N., Poe, A., Kondratov, R. V. Calorie restriction reprograms diurnal rhythms in protein translation to regulate metabolism. FASEB J. 33, 4473–4489 (2019). http://www.fasebj.org

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“…In the end, AfterQC generates a FASTQ file with excellent quality reads only. These files will be referred to as clean FASTQ files henceforth (Chen et al, 2017).…”

Section: Quality Controlmentioning

confidence: 99%

De novo assembly and characterization of transcriptome towards understanding molecular mechanism associated with MYMIV-resistance in Vigna mungo - A computational study

Gupta

Donde

Gouda

et al. 2019

Preprint

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The fast climate change affects yield in Vigna mungo via enhancing both biotic and abiotic stresses.Out of all factors, the yellow mosaic disease has the most damaging effect. However, due to lack of reference genome of Vigna mungo, the complete mechanism associated with MYMIV (Mungbean Yellow Mosaic Indian Virus) resistance in Vigna mungo remain elusive to date. Considering this, the authors made an attempt to release new transcriptome and its annotation by employing computational approaches. Quality assessment of the generated transcriptomes reveals that it successfully aligned with 99.03% of the raw reads and hence can be employed for future research. Functional annotation of the transcriptome reveals that 31% and ~14% of the total transcripts encode lncRNAs and protein-coding sequences, respectively. Further, analysis reveals that, out of total transcripts, only 4536 and 78808 are significantly down and up-regulated during MYMIV infection in Vigna mungo, respectively. These significant transcripts are mainly associated with ribosome, spliceosome, glycolysis /gluconeogenesis, RNA transport, oxidative phosphorylation, protein processing in the endoplasmic reticulum, MAPK signaling pathway -plant, methionine and cysteine metabolism, purine metabolism and RNA degradation. Unlike the previous study, this is for the first time, the present study identified these pathways may play key role in MYMIV resistance in Vigna mungo. Thus, information and transcriptomes data available in the present study make a significant contribution to understanding the genomic structure of Vigna mungo, enabling future analyses as well as downstream applications of gene expression, sequence evolution, and genome annotation.

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AfterQC: automatic filtering, trimming, error removing and quality control for fastq data

Cited by 305 publications

References 17 publications

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Calorie restriction reprograms diurnal rhythms in protein translation to regulate metabolism

De novo assembly and characterization of transcriptome towards understanding molecular mechanism associated with MYMIV-resistance in Vigna mungo - A computational study

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