Enhanced clustering-based differential expression analysis method for RNA-seq data.

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg1 and 2) for identifying DEGs has great potential. However, these methods left room for improvement. This study reports on the improvement (named MBCdeg3). We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (i.e., MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. As MBCdeg3 performs well in many simulation scenarios of RNA-seq count data, MBCdeg3 replaces MBCdeg1 and 2 in our previous report. •MBCdeg3 is a method for both identification and classification of DEGs from RNA-seq count data.•MBCdeg3 is available as a function of R, which is common in the field of expression analysis.•MBCdeg3 performs well in a variety of simulation scenarios for RNA-seq count data.

Synthesis of Short Peptides with Perfluoroalkyl Side Chains and Evaluation of Their Cellular Uptake Efficiency.

With an increasing demand for macromolecular biotherapeutics, the issue of their poor cell-penetrating abilities requires viable and relevant solutions. Herein, we report tripeptides bearing an amino acid with a perfluoroalkyl (RF ) group adjacent to the α-carbon. RF -containing tripeptides were synthesized and evaluated for their ability to transport a conjugated hydrophilic dye (Alexa Fluor 647) into the cells. RF -containing tripeptides with the fluorophore showed high cellular uptake efficiency and none of them were cytotoxic. Interestingly, we demonstrated that the absolute configuration of perfluoroalkylated amino acids (RF -AAs) affects not only nanoparticle formation but also the cell permeability of the tripeptides. These novel RF -containing tripeptides are potentially useful as short and noncationic cell-penetrating peptides (CPPs).

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data.

BACKGROUND: RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis. RESULTS: The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm. CONCLUSIONS: MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

Effects of Daily Hand Activities on Age-Related Declines of Dynamic Motor Function in Individual Fingers.

The present study examined the effects of daily activities of the hands on finger motor function in older adults. Maximum tapping frequency with each finger during single-finger tapping and alternate movements of index-middle, middle-ring, and ring-little finger pairs during double-finger tapping were compared between older adults who used their hands actively in their daily lives and those who did not. The active participants had significantly faster tapping rates for the ring finger in the single-finger tapping and the middle-ring finger pair in the double-finger tapping than did the inactive participants. Thus, daily activity of the hands in older adults could be effective at preventing the loss of dynamic motor function in individual fingers, especially with greater difficulty in movement, resulting from the degeneration with age.

Spatial Accuracy of Predictive Saccades Determines the Performance of Continuous Visuomotor Action.

In a table tennis rally, players perform interceptive actions on a moving ball continuously in a short time, such that the acquisition process of visual information is an important determinant of the performance of the action. However, because it is technically hard to measure gaze movement in a real game, little is known about how gaze behavior is conducted during the continuous visuomotor actions and contributes to the performance. To examine these points, we constructed a novel psychophysical experiment model enabling a continuous visuomotor task without spatial movement of any body parts, including the arm and head, and recorded the movement of the gaze and effector simultaneously at high spatiotemporal resolution. In the task, Gabor patches (target) moved one after another at a constant speed from right to left at random vertical positions on an LC display. Participants hit the target with a cursor moving vertically on the left side of the display by controlling their prehensile force on a force sensor. Participants hit the target with the cursor using a rapid-approaching movement (rapid cursor approach, RCA). Their gaze also showed rapid saccadic approaching movement (saccadic eye approach, SEA), reaching the predicted arrival point of the target earlier than the cursor. The RCA reached in or near the Hit zone in the successful (Hit) trial, but ended up away from it in the unsuccessful (Miss) trial, suggesting the spatial accuracy of the RCA determines the task's success. The SEA in the Hit trial ended nearer the target than the Miss trial. The spatial accuracy of the RCA diminished when the target disappeared 100 ms just after the end of the SEA, suggesting that visual information acquired after the saccade acted as feedback information to correct the cursor movement online for the cursor to reach the target. There was a target speed condition that the target disappearance did not compromise RCA's spatial accuracy, implying the possible RCA correction based on the post-saccadic gaze location information. These experiments clarified that gaze behavior conducted during fast continuous visuomotor actions enables online correction of the ongoing interceptive movement of an effector, improving visuomotor performance.

Gene regulatory network and its constituent transcription factors that control nitrogen-deficiency responses in rice.

Increase in the nitrogen (N)-use efficiency and optimization of N response in crop species are urgently needed. Although transcription factor-based genetic engineering is a promising approach for achieving these goals, transcription factors that play key roles in the response to N deficiency have not been studied extensively. Here, we performed RNA-seq analysis of root samples of 20 Asian rice (Oryza sativa) accessions with differential nutrient uptake. Data obtained from plants exposed to N-replete and N-deficient conditions were subjected to coexpression analysis and machine learning-based pathway inference to dissect the gene regulatory network required for the response to N deficiency. Four transcription factors, including members of the G2-like and bZIP families, were predicted to function as key regulators of gene transcription within the network in response to N deficiency. Cotransfection assays validated inferred novel regulatory pathways, and further analyses using genome-edited knockout lines suggested that these transcription factors are important for N-deficiency responses in planta. Many of the N deficiency-responsive genes, including those encoding key regulators within the network, were coordinately regulated by transcription factors belonging to different families. Transcription factors identified in this study could be valuable for the modification of N response and metabolism.

Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data.

Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other normalization methods (MRN and TCC) were compared using three-group simulation data and real count data. Our findings were as follows: (1) the Bayesian methods coupled with TCC normalization performed comparably or better than those with the default normalization settings under various simulation scenarios, (2) default DE pipelines provided in TCC that implements a generalized linear model framework was still superior to the Bayesian methods with TCC normalization when overall degree of DE was evaluated, and (3) baySeq with TCC was robust against different choices of possible expression patterns. In practice, we recommend using the default DE pipeline provided in TCC for obtaining overall gene ranking and then using the baySeq with TCC normalization for assigning the most plausible expression patterns to individual genes.

TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data.

OBJECTIVE: Differential expression (DE) is a fundamental step in the analysis of RNA-Seq count data. We had previously developed an R/Bioconductor package (called TCC) for this purpose. While this package has the unique feature of an in-built robust normalization method, its use has so far been limited to R users only. There is thus, a need for an alternative to DE analysis by TCC for non-R users. RESULTS: Here, we present a graphical user interface for TCC (called TCC-GUI). Non-R users only need a web browser as the minimum requirement for its use ( https://infinityloop.shinyapps.io/TCC-GUI/ ). TCC-GUI is implemented in R and encapsulated in Shiny application. It contains all the major functionalities of TCC, including DE pipelines with robust normalization and simulation data generation under various conditions. It also contains (i) tools for exploratory analysis, including a useful score termed average silhouette that measures the degree of separation of compared groups, (ii) visualization tools such as volcano plot and heatmap with hierarchical clustering, and (iii) a reporting tool using R Markdown. By virtue of the Shiny-based GUI framework, users can obtain results simply by mouse navigation. The source code for TCC-GUI is available at https://github.com/swsoyee/TCC-GUI under MIT license.

Rhinoceros beetle horn development reveals deep parallels with dung beetles.

Beetle horns are attractive models for studying the evolution of novel traits, as they display diverse shapes, sizes, and numbers among closely related species within the family Scarabaeidae. Horns radiated prolifically and independently in two distant subfamilies of scarabs, the dung beetles (Scarabaeinae), and the rhinoceros beetles (Dynastinae). However, current knowledge of the mechanisms underlying horn diversification remains limited to a single genus of dung beetles, Onthophagus. Here we unveil 11 horn formation genes in a rhinoceros beetle, Trypoxylus dichotomus. These 11 genes are mostly categorized as larval head- and appendage-patterning genes that also are involved in Onthophagus horn formation, suggesting the same suite of genes was recruited in each lineage during horn evolution. Although our RNAi analyses reveal interesting differences in the functions of a few of these genes, the overwhelming conclusion is that both head and thoracic horns develop similarly in Trypoxylus and Onthophagus, originating in the same developmental regions and deploying similar portions of appendage patterning networks during their growth. Our findings highlight deep parallels in the development of rhinoceros and dung beetle horns, suggesting either that both horn types arose in the common ancestor of all scarabs, a surprising reconstruction of horn evolution that would mean the majority of scarab species (~35,000) actively repress horn growth, or that parallel origins of these extravagant structures resulted from repeated co-option of the same underlying developmental processes.

Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results.

BACKGROUND: Hierarchical Sample clustering (HSC) is widely performed to examine associations within expression data obtained from microarrays and RNA sequencing (RNA-seq). Researchers have investigated the HSC results with several possible criteria for grouping (e.g., sex, age, and disease types). However, the evaluation of arbitrary defined groups still counts in subjective visual inspection. RESULTS: To objectively evaluate the degree of separation between groups of interest in the HSC dendrogram, we propose to use Silhouette scores. Silhouettes was originally developed as a graphical aid for the validation of data clusters. It provides a measure of how well a sample is classified when it was assigned to a cluster by according to both the tightness of the clusters and the separation between them. It ranges from 1.0 to - 1.0, and a larger value for the average silhouette (AS) over all samples to be analyzed indicates a higher degree of cluster separation. The basic idea to use an AS is to replace the term cluster by group when calculating the scores. We investigated the validity of this score using simulated and real data designed for differential expression (DE) analysis. We found that larger (or smaller) AS values agreed well with both higher (or lower) degrees of separation between different groups and higher percentages of differentially expressed genes (PDEG). We also found that the AS values were generally independent on the number of replicates (Nrep). Although the PDEG values depended on Nrep, we confirmed that both AS and PDEG values were close to zero when samples in the data showed an intermingled nature between the groups in the HSC dendrogram. CONCLUSION: Silhouettes is useful for exploring data with predefined group labels. It would help provide both an objective evaluation of HSC dendrograms and insights into the DE results with regard to the compared groups.

Finger forces in fastball baseball pitching.

Forces imparted by the fingers onto a baseball are the final, critical aspects for pitching, however these forces have not been quantified previously as no biomechanical technology was available. In this study, an instrumented baseball was developed for direct measurement of ball reaction force by individual fingers and used to provide fundamental information on the forces during a fastball pitch. A tri-axial force transducer with a cable having an easily-detachable connector were installed in an official baseball. Data were collected from 11 pitchers who placed the fingertip of their index, middle, ring, or thumb on the transducer, and threw four-seam fastballs to a target cage from a flat mound. For the index and middle fingers, resultant ball reaction force exhibited a bimodal pattern with initial and second peaks at 38-39ms and 6-7ms before ball release, and their amplitudes were around 97N each. The ring finger and thumb produced single-peak forces of approximately 50 and 83N, respectively. Shear forces for the index and middle fingers formed distinct peak at 4-5ms before release, and the peaks summed to 102N; a kinetic source for backspin on the ball. An additional experiment with submaximal pitching effort showed a linear relationship of peak forces with ball velocity. The peak ball reaction force for fastballs exceeded 80% of maximum finger strength measured, suggesting that strengthening of the distal muscles is important both for enhancing performance and for avoiding injuries.

Comparative analysis of the brain transcriptome in a hyper-aggressive fruit fly, Drosophila prolongata.

Aggressive behavior is observed in many animals, but its intensity differs between species. In a model animal of genetics, Drosophila melanogaster, genetic basis of aggressive behavior has been studied intensively, including transcriptome analyses to identify genes whose expression level was associated with intra-species variation in aggressiveness. However, whether these genes are also involved in the evolution of aggressiveness among different species has not been examined. In this study, we performed de novo transcriptome analysis in the brain of Drosophila prolongata to identify genes associated with the evolution of aggressiveness. Males of D. prolongata were hyper-aggressive compared with closely related species. Comparison of the brain transcriptomes identified 21 differentially expressed genes in males of D. prolongata. They did not overlap with the list of aggression-related genes identified in D. melanogaster, suggesting that genes involved in the evolution of aggressiveness were independent of those associated with the intra-species variation in aggressiveness in Drosophila. Although females of D. prolongata were not aggressive as the males, expression levels of the 21 genes identified in this study were more similar between sexes than between species.

Evaluation of methods for differential expression analysis on multi-group RNA-seq count data.

BACKGROUND: RNA-seq is a powerful tool for measuring transcriptomes, especially for identifying differentially expressed genes or transcripts (DEGs) between sample groups. A number of methods have been developed for this task, and several evaluation studies have also been reported. However, those evaluations so far have been restricted to two-group comparisons. Accumulations of comparative studies for multi-group data are also desired. METHODS: We compare 12 pipelines available in nine R packages for detecting differential expressions (DE) from multi-group RNA-seq count data, focusing on three-group data with or without replicates. We evaluate those pipelines on the basis of both simulation data and real count data. RESULTS: As a result, the pipelines in the TCC package performed comparably to or better than other pipelines under various simulation scenarios. TCC implements a multi-step normalization strategy (called DEGES) that internally uses functions provided by other representative packages (edgeR, DESeq2, and so on). We found considerably different numbers of identified DEGs (18.5 ~ 45.7% of all genes) among the pipelines for the same real dataset but similar distributions of the classified expression patterns. We also found that DE results can roughly be estimated by the hierarchical dendrogram of sample clustering for the raw count data. CONCLUSION: We confirmed the DEGES-based pipelines implemented in TCC performed well in a three-group comparison as well as a two-group comparison. We recommend using the DEGES-based pipeline that internally uses edgeR (here called the EEE-E pipeline) for count data with replicates (especially for small sample sizes). For data without replicates, the DEGES-based pipeline with DESeq2 (called SSS-S) can be recommended.

The impact of aging on the spatial accuracy of quick corrective arm movements in response to sudden target displacement during reaching.

Age-related declines in visuomotor processing speed can have a large impact on motor performance in elderly individuals. Contrary to previous findings, however, recent studies revealed that elderly individuals are able to quickly react to displacement of a visual target during reaching. Here, we investigated the influence of aging on quick, corrective responses to perturbations during reaching in the terms of their functional contribution to accuracy. Elderly and young adults performed reaching movements to a visual target that could be displaced during reaching, and they were requested to move their hand to reach the final target location as quickly as possible. Results showed that, for the younger group, the variance in the directional error of the corrective response correlated with the variance in the reaching trajectory at the halfway point of the reach, but the correlation decreased at the end of the reaching. On the other hand, such correlations were not significant in elderly participants, although the variance of the directional error did not show a significant difference between age groups. Thus, the quick, corrective response seems to play an important role in decreasing variability, especially before the end of reaching, and aging can impair this process.

Control of Precision Grip Force in Lifting and Holding of Low-Mass Objects.

Few studies have investigated the control of grip force when manipulating an object with an extremely small mass using a precision grip, although some related information has been provided by studies conducted in an unusual microgravity environment. Grip-load force coordination was examined while healthy adults (N = 17) held a moveable instrumented apparatus with its mass changed between 6 g and 200 g in 14 steps, with its grip surface set as either sandpaper or rayon. Additional measurements of grip-force-dependent finger-surface contact area and finger skin indentation, as well as a test of weight discrimination, were also performed. For each surface condition, the static grip force was modulated in parallel with load force while holding the object of a mass above 30 g. For objects with mass smaller than 30 g, on the other hand, the parallel relationship was changed, resulting in a progressive increase in grip-to-load force (GF/LF) ratio. The rayon had a higher GF/LF force ratio across all mass levels. The proportion of safety margin in the static grip force and normalized moment-to-moment variability of the static grip force were also elevated towards the lower end of the object mass for both surfaces. These findings indicate that the strategy of grip force control for holding objects with an extremely small mass differs from that with a mass above 30 g. The data for the contact area, skin indentation, and weight discrimination suggest that a decreased level of cutaneous feedback signals from the finger pads could have played some role in a cost function in efficient grip force control with low-mass objects. The elevated grip force variability associated with signal-dependent and internal noises, and anticipated inertial force on the held object due to acceleration of the arm and hand, could also have contributed to the cost function.

Impact of denervation-induced muscle atrophy on housekeeping gene expression in mice.

INTRODUCTION: Immobilization induced by experimental denervation leads to rapid and progressive alterations in structural and biochemical properties of skeletal muscle. Real-time reverse transcription-polymerase chain reaction (RT-PCR) is a popular method of elucidating the molecular mechanisms involved in muscle atrophy. Identification of suitable reference genes that are not affected by experimental conditions is a critical step in accurate normalization of real-time RT-PCR. METHODS: We investigated the impact of denervation-induced muscle atrophy for 2 weeks on the expression of common housekeeping genes. RESULTS: Denervation differentially affected the expression levels of these genes. RefFinder software identified TATA box binding protein (Tbp) as the most stable gene and showed that the stability of glyceraldehyde-3-phosphate dehydrogenase (Gapdh) and hypoxanthine guanine phosphoribosyl transferase (Hprt) genes was low, even though they are widely used for normalization. CONCLUSIONS: The appropriate reference gene for normalization of genes of interest in denervated muscle is Tbp.

Two types of motor strategy for accurate dart throwing.

This study investigated whether expert dart players utilize hand trajectory patterns that can compensate for the inherent variability in their release timing. In this study, we compared the timing error and hand trajectory patterns of expert players with those of novices. Eight experts and eight novices each made 60 dart throws, aiming at the bull's-eye. The movements of the dart and index finger were captured using seven 480-Hz cameras. The data were interpolated using a cubic spline function and analyzed by the millisecond. The estimated vertical errors on the dartboard were calculated as a time-series by using the state variables of the index finger (position, velocity, and direction of motion). This time-series error represents the hand trajectory pattern. Two variables assessing the performance outcome in the vertical plane and two variables related to the timing control were quantified on the basis of the time-series error. The results revealed two typical types of motor strategies in the expert group. The timing error of some experts was similar to that of novices; however, these experts had a longer window of time in which to release an accurately thrown dart. These subjects selected hand trajectory patterns that could compensate for the timing error. Other experts did not select the complementary hand trajectories, but greatly reduced their error in release timing.

Joint action syntax in Japanese martial arts.

Participation in interpersonal competitions, such as fencing or Japanese martial arts, requires players to make instantaneous decisions and execute appropriate motor behaviors in response to various situations. Such actions can be understood as complex phenomena emerging from simple principles. We examined the intentional switching dynamics associated with continuous movement during interpersonal competition in terms of their emergence from a simple syntax. Linear functions on return maps identified two attractors as well as the transitions between them. The effects of skill differences were evident in the second- and third-order state-transition diagrams for these two attractors. Our results suggest that abrupt switching between attractors is related to the diverse continuous movements resulting from quick responses to sudden changes in the environment. This abrupt-switching-quick-response behavior is characterized by a joint action syntax. The resulting hybrid dynamical system is composed of a higher module with discrete dynamics and a lower module with continuous dynamics. Our results suggest that intelligent human behavior and robust autonomy in real-life scenarios are based on this hybrid dynamical system, which connects interpersonal coordination and competition.

TCC: an R package for comparing tag count data with robust normalization strategies.

BACKGROUND: Differential expression analysis based on "next-generation" sequencing technologies is a fundamental means of studying RNA expression. We recently developed a multi-step normalization method (called TbT) for two-group RNA-seq data with replicates and demonstrated that the statistical methods available in four R packages (edgeR, DESeq, baySeq, and NBPSeq) together with TbT can produce a well-ranked gene list in which true differentially expressed genes (DEGs) are top-ranked and non-DEGs are bottom ranked. However, the advantages of the current TbT method come at the cost of a huge computation time. Moreover, the R packages did not have normalization methods based on such a multi-step strategy. RESULTS: TCC (an acronym for Tag Count Comparison) is an R package that provides a series of functions for differential expression analysis of tag count data. The package incorporates multi-step normalization methods, whose strategy is to remove potential DEGs before performing the data normalization. The normalization function based on this DEG elimination strategy (DEGES) includes (i) the original TbT method based on DEGES for two-group data with or without replicates, (ii) much faster methods for two-group data with or without replicates, and (iii) methods for multi-group comparison. TCC provides a simple unified interface to perform such analyses with combinations of functions provided by edgeR, DESeq, and baySeq. Additionally, a function for generating simulation data under various conditions and alternative DEGES procedures consisting of functions in the existing packages are provided. Bioinformatics scientists can use TCC to evaluate their methods, and biologists familiar with other R packages can easily learn what is done in TCC. CONCLUSION: DEGES in TCC is essential for accurate normalization of tag count data, especially when up- and down-regulated DEGs in one of the samples are extremely biased in their number. TCC is useful for analyzing tag count data in various scenarios ranging from unbiased to extremely biased differential expression. TCC is available at http://www.iu.a.u-tokyo.ac.jp/~kadota/TCC/ and will appear in Bioconductor (http://bioconductor.org/) from ver. 2.13.