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Additional file 4: Fig. S1. The microbial reads percentage of EBV samples and EV71 samples. A. The microbial reads percentage of EBV samples in methods Total DNA , Total NA ,Total RNA and WTA. B. The microbial reads percentage of EV71 samples in methods Total NA ,Total RNA and WTA. Fig. S2. NR, RPMratio and Coverage percent of target pathogens. Subfigure A/B/C shows NR/ RPMratio/coverage percentage of E. coli respectively comparing four sample pretreatment/nuclear acid extraction methods including Microbial DNA, Total DNA, Total NA and Total RNA. The red dots represent concentration of 106cfu/ml and the blue dots represent concentration of 5x104 cfu/ml. Subfigure D/E/F shows NR/ RPMratio/coverage percentage of SP comparing four sample pretreatment/nuclear acid extraction methods respectively. The red dots represent concentration of 106cfu/ml and the blue dots represent concentration of 5x104 cfu/ml. Subfigure G/H/I shows NR/RPMratio/coverage percentage of EBV respectively comparing four sample pretreatment/nuclear acid extraction methods including Total DNA, Total NA, Total RNA and WTA. The red dots represent concentration of 5x105 copies/ml and the blue dots represent concentration of 5x103 copies/ml. Subfigure J/K/L shows NR/RPMratio/coverage percentage of EV71 respectively comparing four sample pretreatment/nuclear acid extraction methods respectively. The red dots represent concentration of 5x105 copies/ml and the blue dots represent concentration of 5x103 copies/ml. Fig. S3. Genomic coverage of pathogens in CSF from encephalitis patients. A. Genomic coverage of Echovirus 30 in clinical CSF 0006. B. Genomic coverage of coxsackievirus B5 in clinical CSF 0010. Fig. S4. Genomic coverage of Streptococcus pneumoniae, Streptococcus thermophilus and Mogibacterium diversum in sample Microbial DNA-ILL -1. A. Genomic coverage of Streptococcus pneumoniae. B. Genomic coverage of Streptococcus thermophilus. C. Genomic coverage of Mogibacterium diversum.

Additional file 1; Figure S1. The genomic locations of fusion partners across the genome. Figure S2. Comparison of spatial distances between leukemia fusion gene pairs (from TumorFusions) and control. Figure S3. Comparisons between FISH-based results and single-cell -based results for common KMT2A partners. Figure S4. Comparisons of spatial distances of fusion gene pairs associated with ALL and AML. Figure S5. ETO-induced DSB profiles around other CRGs in TK6 cells. Figure S6. Analysis of bulk Hi-C data from GM12878 and K562 cell lines.

Additional file 3: Table S3. Metagenomic sequencing results of each sample.

Additional file 1: Table S1. Sample ID and Pathogens contained.

Background Although the prognosis of chronic myeloid leukemia (CML) has dramatically improved, the pathogenesis of CML remains elusive. Studies have shown that sustained phosphorylation of AKT1 plays a crucial role in the proliferation of CML cells. Evidence indicates that in tongue cancer cells, FAM168A, also known as tongue cancer resistance-associated protein (TCRP1), can directly bind to AKT1 and regulate AKT1/NFκB signaling pathways. This study aimed to investigate the role of FAM168A in regulation of AKT1/NFκB signaling pathway and cell cycle in CML. Methods FAM168A interference was performed, and the expression and phosphorylation of FAM168A downstream proteins were measured in K562 CML cell line. The possible roles of FAM168A in the proliferation of CML cells were investigated using in vitro cell culture, in vivo animal models and clinical specimens. Results We found that the expression of FAM168A significantly increased in the peripheral blood mononuclear cells of CML patients, compared with normal healthy controls. FAM168A interference did not change AKT1 protein expression, but significantly decreased AKT1 phosphorylation, significantly increased IκB-α protein level, and significantly reduced nuclear NFκB protein level. Moreover, there was a significant increase of G2/M phase cells and Cyclin B1 level. Immunoprecipitation results showed that FAM168A interacts with breakpoint cluster region (BCR) -Abelson murine leukemia (ABL1) fusion protein and AKT1, respectively. Animal experiments confirmed that FAM168A interference prolonged the survival and reduced the tumor formation in mice inoculated with K562 cells. The results of clinical specimens showed that FAM168A expression and AKT1 phosphorylation were significantly elevated in CML patients. Conclusion This study demonstrates that FAM168A may act as a linker protein that binds to BCR-ABL1 and AKT1, which further mediates the downstream signaling pathways in CML. Our findings demonstrate that FAM168A may be involved in the regulation of AKT1/NFκB signaling pathway and cell cycle in CML. Electronic supplementary material The online version of this article (10.1186/s12885-019-5898-4) contains supplementary material, which is available to authorized users.

An all-k-nearest-neighbor (AkNN) query finds k nearest neighbors for each query object. This problem arises naturally in many areas, such as GIS (geographic information system), multimedia retrieval, and recommender systems. To support various data types and flexible distance metrics involved in real applications, we study AkNN retrieval in metric spaces, namely, metric AkNN (MAkNN) search. Consider that the underlying indexes on the query set and the object set may not exist, which is natural in many scenarios. For example, the query set and the object set could be the results of other queries, and thus, the underlying indexes cannot be built in advance. To support MAkNN search on datasets without any underlying index, we propose an efficient disk-based algorithm, termed as Partition-Based MAkNN Algorithm (PMA), which follows a partition-search framework and employs a series of pruning rules for accelerating the search. In addition, we extend our techniques to tackle an interesting variant of MAkNN queries, i.e., metric self-AkNN (MSAkNN) search, where the query set is identical to the object set. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of our pruning rules and the efficiency of the proposed algorithms, compared with state-of-the-art MAkNN and MSAkNN algorithms.