GeFaST: An improved method for OTU assignment by generalising Swarm's fastidious clustering approach

The impact of FASTQ and alignment read order on structural variant calling from long-read sequencing data

Background

Structural variant (SV) calling from DNA sequencing data has been challenging due to several factors, including the ambiguity of short-read alignments, multiple complex SVs in the same genomic region, and the lack of "truth" datasets for benchmarking. Additionally, caller choice, parameter settings, and alignment method are known to affect SV calling. However, the impact of FASTQ read order on SV calling has not been explored for long-read data.

Results

Here, we used PacBio DNA sequencing data from 15 Caenorhabditis elegans strains and four Arabidopsis thaliana ecotypes to evaluate the sensitivity of different SV callers on FASTQ read order. Comparisons of variant call format files generated from the original and permutated FASTQ files demonstrated that the order of input data affected the SVs predicted by each caller. In particular, pbsv was highly sensitive to the order of the input data, especially at the highest depths where over 70% of the SV calls generated from pairs of differently ordered FASTQ files were in disagreement. These demonstrate that read order sensitivity is a complex, multifactorial process, as the differences observed both within and between species varied considerably according to the specific combination of aligner, SV caller, and sequencing depth. In addition to the SV callers being sensitive to the input data order, the SAMtools alignment sorting algorithm was identified as a source of variability following read order randomization.

Conclusion

The results of this study highlight the sensitivity of SV calling on the order of reads encoded in FASTQ files, which has not been recognized in long-read approaches. These findings have implications for the replication of SV studies and the development of consistent SV calling protocols. Our study suggests that researchers should pay attention to the input order sensitivity of read alignment sorting methods when analyzing long-read sequencing data for SV calling, as mitigating a source of variability could facilitate future replication work. These results also raise important questions surrounding the relationship between SV caller read order sensitivity and tool performance. Therefore, tool developers should also consider input order sensitivity as a potential source of variability during the development and benchmarking of new and improved methods for SV calling.

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

Background

Sequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering and Microphenoor DiTaxa features) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction.

Results

On two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings. Porphyromonadaceae, Ruminococcaceae, and an unnamed species of Blastocystis were significantly enriched in autism, while Veillonellaceae was significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observed Megasphaera andSutterellaceae highly enriched in obesity, and Phocaeicola significantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84.

Conclusions

SBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded from http://github.com/briannachrisman/16s_biomarkers.

On the use of sequence-quality information in OTU clustering

Background

High-throughput sequencing has become an essential technology in life science research. Despite continuous improvements in technology, the produced sequences are still not entirely accurate. Consequently, the sequences are usually equipped with error probabilities. The quality information is already employed to find better solutions to a number of bioinformatics problems (e.g. read mapping). Data processing pipelines benefit in particular (especially when incorporating the quality information early), since enhanced outcomes of one step can improve all subsequent ones. Preprocessing steps, thus, quite regularly consider the sequence quality to fix errors or discard low-quality data. Other steps, however, like clustering sequences into operational taxonomic units (OTUs), a common task in the analysis of microbial communities, are typically performed without making use of the available quality information.

Results

In this paper, we present quality-aware clustering methods inspired by quality-weighted alignments and model-based denoising, and explore their applicability to OTU clustering. We implemented the quality-aware methods in a revised version of our de novo clustering tool GeFaST and evaluated their clustering quality and performance on mock-community data sets. Quality-weighted alignments were able to improve the clustering quality of GeFaST by up to 10%. The examination of the model-supported methods provided a more diverse picture, hinting at a narrower applicability, but they were able to attain similar improvements. Considering the quality information enlarged both runtime and memory consumption, even though the increase of the former depended heavily on the applied method and clustering threshold.

Conclusions

The quality-aware methods expand the iterative, de novo clustering approach by new clustering and cluster refinement methods. Our results indicate that OTU clustering constitutes yet another analysis step benefiting from the integration of quality information. Beyond the shown potential, the quality-aware methods offer a range of opportunities for fine-tuning and further extensions.