Performance evaluation of lossy quality compression algorithms for RNA-seq data

Background Recent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear. Results In this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels. Conclusions Lossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results. Document type: Articl

The nuclear phosphatase SCP4 regulates FoxO transcription factors during muscle wasting in chronic kidney disease

Chronic kidney disease (CKD) and related inflammatory responses stimulate protein-energy wasting, a complication causing loss of muscle mass. Primarily, muscle wasting results from accelerated protein degradation via autophagic/lysosomal and proteasomal pathways, but mechanisms regulating these proteolysis pathways remain unclear. Since dephosphorylation of FoxOs regulates ubiquitin/proteasome protein metabolism, we tested whether a novel nuclear phosphatase, the small C-terminal domain phosphatase (SCP) 4, regulates FoxOs signaling and, in turn, muscle wasting. In cultured mouse myoblast cells, SCP4 overexpression stimulated proteolysis, while knockdown of SCP4 prevented the proteolysis stimulated by inflammatory cytokines. SCP4 overexpression led to nuclear accumulation of FoxO1/3a followed by increased expression of catabolic factors including myostatin, Atrogin-1, and MuRF-1, and induction of lysosomal-mediated proteolysis. Treatment of C2C12 myotubes with proinflammatory cytokines stimulated SCP4 expression in an NF-\u3baB-dependent manner. In skeletal muscle of mice with CKD, SCP4 expression was up-regulated. Similarly, in skeletal muscle of patients with CKD, SCP4 expression was significantly increased. Knockdown of SCP4 significantly suppressed FoxO1/3a-mediated expression of Atrogin-1 and MuRF-1 and prevented muscle wasting in mice with CKD. Thus, SCP4 is a novel regulator of FoxO transcription factors and promotes cellular proteolysis. Hence, targeting SCP4 may prevent muscle wasting in CKD and possibly other catabolic conditions

Using random forest algorithm for glomerular and tubular injury diagnosis

ObjectivesChronic kidney disease (CKD) is a common chronic condition with high incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early manifestations of CKD and could indicate the risk of its development. In this study, we aimed to classify GI and TI using three machine learning algorithms to promote their early diagnosis and slow the progression of CKD.MethodsDemographic information, physical examination, blood, and morning urine samples were first collected from 13,550 subjects in 10 counties in Shanxi province for classification of GI and TI. Besides, LASSO regression was employed for feature selection of explanatory variables, and the SMOTE (synthetic minority over-sampling technique) algorithm was used to balance target datasets, i.e., GI and TI. Afterward, Random Forest (RF), Naive Bayes (NB), and logistic regression (LR) were constructed to achieve classification of GI and TI, respectively.ResultsA total of 12,330 participants enrolled in this study, with 20 explanatory variables. The number of patients with GI, and TI were 1,587 (12.8%) and 1,456 (11.8%), respectively. After feature selection by LASSO, 14 and 15 explanatory variables remained in these two datasets. Besides, after SMOTE, the number of patients and normal ones were 6,165, 6,165 for GI, and 6,165, 6,164 for TI, respectively. RF outperformed NB and LR in terms of accuracy (78.14, 80.49%), sensitivity (82.00, 84.60%), specificity (74.29, 76.09%), and AUC (0.868, 0.885) for both GI and TI; the four variables contributing most to the classification of GI and TI represented SBP, DBP, sex, age and age, SBP, FPG, and GHb, respectively.ConclusionRF boasts good performance in classifying GI and TI, which allows for early auxiliary diagnosis of GI and TI, thus facilitating to help alleviate the progression of CKD, and enjoying great prospects in clinical practice

Study of the Effect of Mold Corner Shape on the Initial Solidification Behavior of Molten Steel Using Mold Simulator

The chamfered mold with a typical corner shape (angle between the chamfered face and hot face is 45 deg) was applied to the mold simulator study in this paper, and the results were compared with the previous results from a well-developed right-angle mold simulator system. The results suggested that the designed chamfered structure would increase the thermal resistance and weaken the two-dimensional heat transfer around the mold corner, causing the homogeneity of the mold surface temperatures and heat fluxes. In addition, the chamfered structure can decrease the fluctuation of the steel level and the liquid slag flow around the meniscus at mold corner. The cooling intensities at different longitudinal sections of shell are close to each other due to the similar time-average solidification factors, which are 2.392 mm/s1/2 (section A-A: chamfered center), 2.372 mm/s1/2 (section B-B: 135 deg corner), and 2.380 mm/s1/2 (section D-D: face), respectively. For the same oscillation mark (OM), the heights of OM roots at different positions (profile L1 (face), profile L2 (135 deg corner), and profile L3 (chamfered center)) are very close to each other. The average value of height difference (HD) between two OMs roots for L1 and L2 is 0.22 mm, and for L2 and L3 is 0.38 mm. Finally, with the help of metallographic examination, the shapes of different hooks were also discussed

Optical Single-Sideband WDM Nyquist 32-QAM SC-FDE Transmission system with Direct Detection

125 Gbit/s optical single side band (OSSB) Square-32QAM wavelength-divisionmultiplexed direct detection over 75.1km SSMF transmission has been successfully demonstrated with a subcarrier frequency band of 2.9 GHz and a channel frequency grid of 12.5GHz. ? OSA 2014.EI