4 research outputs found
Functional Enrichment Analysis of Transcriptomics Data of Breast Cancer RNA-seq
The biochemical mechanism driving cancer metastasis and primary cancer invasion of new sites are still unclear as the process can be complex. The mutations in somatic cells often include mutation drivers and some passenger mutations. In this study, we have analyzed RNA-Seq datasets from primary breast cancer and metastatic lung cancer for differentially expressed gene lists to gain insight into transcriptomic profiles of the two conditions. The gene lists are analyzed for pathway and functional enrichment annotations. It is interesting to note that the top enriched pathways are major ones involving some connected cancer-related signaling processes. The enriched gene sets from this analysis includes ones connected to cancer proliferation, progression, and metastatic invasions. The pathways and genes show some overlapping networks and connections that may be key to finding potential mutation driver genes
A rigorous benchmarking of methods for SARS-CoV-2 lineage abundance estimation in wastewater
In light of the continuous transmission and evolution of SARS-CoV-2 coupled
with a significant decline in clinical testing, there is a pressing need for
scalable, cost-effective, long-term, passive surveillance tools to effectively
monitor viral variants circulating in the population. Wastewater genomic
surveillance of SARS-CoV-2 has arrived as an alternative to clinical genomic
surveillance, allowing to continuously monitor the prevalence of viral lineages
in communities of various size at a fraction of the time, cost, and logistic
effort and serving as an early warning system for emerging variants, critical
for developed communities and especially for underserved ones. Importantly,
lineage prevalence estimates obtained with this approach aren't distorted by
biases related to clinical testing accessibility and participation. However,
the relative performance of bioinformatics methods used to measure relative
lineage abundances from wastewater sequencing data is unknown, preventing both
the research community and public health authorities from making informed
decisions regarding computational tool selection. Here, we perform
comprehensive benchmarking of 18 bioinformatics methods for estimating the
relative abundance of SARS-CoV-2 (sub)lineages in wastewater by using data from
36 in vitro mixtures of synthetic lineage and sublineage genomes. In addition,
we use simulated data from 78 mixtures of lineages and sublineages co-occurring
in the clinical setting with proportions mirroring their prevalence ratios
observed in real data. Importantly, we investigate how the accuracy of the
evaluated methods is impacted by the sequencing technology used, the associated
error rate, the read length, read depth, but also by the exposure of the
synthetic RNA mixtures to wastewater, with the goal of capturing the effects
induced by the wastewater matrix, including RNA fragmentation and degradation.Comment: For correspondence: [email protected]