LAUSR

Next-Generation Sequencing-driven analysis and Systems Biology approaches commonly serve as a backdrop for a study of a tumor genome. This issue of BMC Medical Genomics SBB-2019 (“Systems Biology and Bioinformatics”) presents recent works discussed at the 11th Young Scientists School “Systems Biology and Bioinformatics”-2019, held in Novosibirsk, Russia (http://conf.bionet.nsc.ru/sbb2 019/en/). Here we collated some cancer gene expression studies, some mutation profiling studies as well as some insightful case reports. The SBB school series on bioinformatics proceeds annually since 2008 under the joint steerage of the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences and Novosibirsk State University [1, 2]. We had publications in special topic issues after the Schools before in BMC Genomics, BMC Medical Genomics and related BioMed Central family journals since 2014 [3–5]. The SBB Schools in Novosibirsk were initially conceived as satellite event for young scientists held at the same time as BGRS\SB (Bioinformatics of Genome Regulation and Structure \ Systems Biology) conference series, since 1998 taking place biannually. The recent BGRS\SB-2020 event in Novosibirsk was over at the time of the current journal issue publication (https://bgrssb.icgbio.ru/2020/). Other special issues (Supplements) to the BMC journals in the fields of genomics, genetics, bioinformatics, and medical genetics are published at BMC Genomics, BMC Genetics and three other BMC journals. The BGRS\SB-2018 conference highlights were published in 2018 [5–7], and continued the BMC Medical Genomics special issues in 2019 [8]. Public discussion of the conference presentations at the open access platforms of BioMed Central and other publishers serve as an international educational resource for young scientists [9, 10]. The articles comprising this issue of BMC Medical Genomics are focused on cancer genomics. Using transcriptomic data, bioinformatic models can be built for patientoriented ranking of cancer drugs [11]. Nicolas Borisov et al. [12] (this issue) developed the database for cancer gene expression profiles associated with clinical outcomes of the chemotherapy treatments. Authors mined Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomicsdriven Therapy (TARGET) repositories to pull a database of 2786 gene expression profiles associated with clinical responses on chemotherapy. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors and suitable for Machine Learning analysis of these malignancy. Alexander Lavrov and co-authors [13] (this issue) review pathogenic variants targetable by single base editing. Single nucleotide variants account for approximately 90% of all known pathogenic variants responsible for human diseases, including thousands of known 6000 monogenic diseases. Recently discovered CRISPR/Cas9 base editors are capable of correcting individual nucleotide positions, thus, providing opportunities for personalized therapy. Unfortunately, none of such editors are perfect in their specificity [14]. Authors summarized all possible pathogenic variants which may be efficiently targeted by each of the known base editors. They analyzed 21 editing system currently reported in 9 publications and showed that C > T base editors are capable of precisely targeting about 3200 mutations, a total of