Clinical applications of precision oncology require accurate tests that can distinguish true cancer specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Data availabilitySummary statistics generated by COVID-19 Host Genetics Initiative are available online (https://www.covid19hg.org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table 3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table 1.

We performed whole transcriptome analysis of osteosarcoma bone samples. Initially, we sequenced total RNA from 36 fresh-frozen samples (18 tumoral bone samples and 18 non-tumoral paired samples) matching in pairs for each osteosarcoma patient. We also performed independent gene expression analysis of formalin-fixed paraffin-embedded samples to verify the RNAseq results. Formalin-fixed paraffin-embedded samples allowed us to analyze the effect of chemotherapy. Data were analyzed with DESeq2, edgeR and Reactome packages of R. We found 5365 genes expressed differentially between the normal bone and osteosarcoma tissues with an FDR below 0.05, of which 3399 genes were upregulated and 1966 were downregulated. Among those genes, BTNL9, MMP14, ABCA10, ACACB, COL11A1, and PKM2 were expressed differentially with the highest significance between tumor and normal bone. Functional annotation with the reactome identified significant changes in the pathways related to the extracellular matrix degradation and collagen biosynthesis. It was suggested that chemotherapy may induce the modification of ECM with important collagen biosynthesis. Taken together, our results indicate that changes in the degradation of extracellular matrix seem to be an important mechanism of osteosarcoma and efficient chemotherapy induces the genes related to bone formation. Impact statement Osteosarcoma is a rare disease but it is of interest to many scientists all over the world because the current standard treatment still has poor results. We sequenced total RNA from 36 fresh-frozen paired samples (18 tumoral bone samples and 18 non-tumoral paired samples) from osteosarcoma patients. We found that differences in the gene expressions between the normal and affected bones reflected the changes in the regulation of the degradation of collagen and extracellular matrix. We believe that these findings contribute to the understanding of OS and suggest ideas for further studies.

BackgroundIn present study we performed whole transcriptome analysis in plaque psoriasis patients and compared lesional skin with non-lesional skin and with the skin from healthy controls. We sequenced total RNA from 12 lesional (LP), 12 non-lesional (NLP) and from 12 normal (C) skin biopsies.ResultsCompared with previous gene expression profiling studies we had three groups under analysis - LP, NLP and C. Using NLP samples allows to see the transcriptome of visually normal skin from psoriasis patient. In LP skin S100A12, S100A7A, LCE3E, DEFB4A, IL19 were found up regulated. In addition to already these well-described genes, we also found several other genes related to psoriasis. Namely, KLK9, OAS2, OAS3, PLA2G, IL36G, IL36RN were found to be significantly and consistently related to the psoriatic lesions and this finding is supported also by previous studies. The genes up-regulated in the LP samples were related to the innate immunity, IL17 and IL10 networks. In NLP samples innate immunity and IL17 network were activated, but activation of IL10 network was not evident. The transcriptional changes characteristic in the NLP samples can be considered as a molecular signature of “dormant psoriasis”.ConclusionsTaken together, our study described the transcriptome profile characteristic for LP and NLP psoriatic skin. RNA profile of the NLP skin is in between the lesional and healthy skin, with its own specific pattern. We found that both LP and NLP have up-regulated IL17 network, whereas LP skin has up regulated IL10 related cytokines (IL19, IL20, IL24). Moreover, IL36G and IL36RN were identified as strong regulators of skin pathology in both LP and NLP skin samples, with stronger influence in LP samples.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1508-2) contains supplementary material, which is available to authorized users.