Biology subjects

Ines Hellmann

Publications and source records attributed to Ines Hellmann.

3 recordsLinked to original sources

bioRxiv2016

Comparative analysis of single-cell RNA sequencing methods

Single-cell RNA sequencing (scRNA-seq) offers exciting possibilities to address biological and medical questions, but a systematic comparison of recently developed protocols is still lacking. Here, we generated data from 447 mouse embryonic stem cells using Drop-seq, SCRB-seq, Smart-seq (on Fluidigm C1) and Smart-seq2 and analyzed existing data from 35 mouse embryonic stem cells prepared with CEL-seq. We find that Smart-seq2 is the most sensitive method as it detects the most genes per cell and across cells with the most even coverage, well suited for annotating transcriptomes. However, we also find that unique molecular identifiers (UMIs), available for CEL-seq, Drop-seq and SCRB-seq, reduce the measurement noise considerably, which is most relevant for quantifying transcriptomes. Importantly, we show by power simulations that SCRB-seq and Drop-seq are the most cost-efficient methods for detecting differentially expressed genes. Our analyses offer a solid basis for an informed choice among five prominent scRNA-seq protocols and for future evaluations of protocol improvements.

Genomics↗

bioRxiv2015

The impact of amplification on differential expression analyses by RNA-seq

Currently quantitative RNA-Seq methods are pushed to work with increasingly small starting amounts of RNA that require amplification. However, it is unclear how much noise or bias amplification introduces and how this effects precision and accuracy of RNA quantification. To assess the effects of amplification, reads that originated from the same RNA molecule (PCR-duplicates) need to be identified. Computationally, read duplicates are defined via their mapping position, which does not distinguish PCR- from natural duplicates and hence it is unclear how to treat duplicate reads.\n\nHere, we generate and analyse RNA-seq datasets prepared using three different protocols (Smart-Seq, TruSeq and UMI-seq). We find that a large fraction of computationally identified read duplicates can be explained by sampling and fragmentation bias. Consequently, the computational removal of duplicates does not improve accuracy, power or FDR, but can actually worsen them. Even when duplicates are experimentally identified by unique molecular identifiers (UMIs), power and FDR are only mildly improved. However, the pooling of samples as made possible by the early barcoding of the UMI-protocol leads to an appreciable increase in the power to detect differentially expressed genes.

Genomics↗

bioRxiv2015

Detecting recent selective sweeps while controlling for mutation rate and background selection

A composite likelihood ratio test implemented in the program SweepFinder is a commonly used method for scanning a genome for recent selective sweeps. SweepFinder uses information on the spatial pattern of the site frequency spectrum (SFS) around the selected locus. To avoid confounding effects of background selection and variation in the mutation process along the genome, the method is typically applied only to sites that are variable within species. However, the power to detect and localize selective sweeps can be greatly improved if invariable sites are also included in the analysis. In the spirit of a Hudson-Kreitman-Aguade test, we suggest to add fixed differences relative to an outgroup to account for variation in mutation rate, thereby facilitating more robust and powerful analyses. We also develop a method for including background selection modeled as a local reduction in the effective population size. Using simulations we show that these advances lead to a gain in power while maintaining robustness to mutation rate variation. Furthermore, the new method also provides more precise localization of the causative mutation than methods using the spatial pattern of segregating sites alone.

Evolutionary Biology↗