With the advances of next-generation sequencing technology, the field of disease research has been revolutionized. However, pinpointing the disease-causing variants from millions of revealed variants is still a tough task. Here, we have reviewed the existing linkage analysis tools and presented PedMiner, a web-based application designed to narrow down candidate variants from family based whole-exome sequencing (WES) data through linkage analysis. PedMiner integrates linkage analysis, variant annotation and prioritization in one automated pipeline. It provides graphical visualization of the linked regions along with comprehensive annotation of variants and genes within these linked regions. This efficient and comprehensive application will be helpful for the scientific community working on Mendelian inherited disorders using family based WES data.

SUMMARY: Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in the last decades. Here, we present CPSS 2.0, an updated version of CPSS 1.0 for small RNA sequencing data analysis, with the following improvements: (i) a substantial increase of supported species from 10 to 48; (ii) improved strategies applied to detect ncRNAs; (iii) more ncRNAs can be detected and profiled, such as lncRNA and circRNA; (iv) identification of differentially expressed ncRNAs among multiple samples; (v) enhanced visualization interface containing graphs and charts in detailed analysis results. The new version of CPSS is an efficient bioinformatics tool for users in non-coding RNA research. AVAILABILITY AND IMPLEMENTATION: CPSS 2.0 is implemented in PHP + Perl + R and can be freely accessed at http://114.214.166.79/cpss2.0/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Copy number variations (CNVs) are the main genetic structural variations in cancer genome. Detecting CNVs in genetic exome region is efficient and cost-effective in identifying cancer associated genes. Many tools had been developed accordingly and yet these tools lack of reliability because of high false negative rate, which is intrinsically caused by genome exonic bias. RESULTS: To provide an alternative option, here, we report Anaconda, a comprehensive pipeline that allows flexible integration of multiple CNV-calling methods and systematic annotation of CNVs in analyzing WES data. Just by one command, Anaconda can generate CNV detection result by up to four CNV detecting tools. Associated with comprehensive annotation analysis of genes involved in shared CNV regions, Anaconda is able to deliver a more reliable and useful report in assistance with CNV-associate cancer researches. CONCLUSION: Anaconda package and manual can be freely accessed at http://mcg.ustc.edu.cn/bsc/ANACONDA/ .

UNLABELLED: : Next-Generation Sequencing (NGS) technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent differences from their corresponding mature reference sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). These isomiRs mainly originate via the imprecise and alternative cleavage during the pre-miRNA processing and post-transcriptional modifications that influence miRNA stability, their sub-cellular localization and target selection. Although several tools for the identification of isomiR have been reported, no bioinformatics resource dedicated to gather isomiRs from public NGS data and to provide functional analysis of these isomiRs is available to date. Thus, a free online database, IsomiR Bank has been created to integrate isomiRs detected by our previously published algorithm CPSS. In total, 2727 samples (Small RNA NGS data downloaded from ArrayExpress) from eight species (Arabidopsis thaliana, Drosophila melanogaster, Danio rerio, Homo sapiens, Mus musculus, Oryza sativa, Solanum lycopersicum and Zea mays) are analyzed. At present, 308 919 isomiRs from 4706 mature miRNAs are collected into IsomiR Bank. In addition, IsomiR Bank provides target prediction and enrichment analysis to evaluate the effects of isomiRs on target selection. AVAILABILITY AND IMPLEMENTATION: IsomiR Bank is implemented in PHP/PERL + MySQL + R format and can be freely accessed at http://mcg.ustc.edu.cn/bsc/isomir/ CONTACTS: : [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this study, the authors propose an improved frequency‐domain fractionally spaced (FDFS) minimum mean square error (MMSE) receiver for zero‐padded multi‐carrier code division multiple access (MC‐CDMA) systems when the guard interval is not enough to avoid the inter‐symbol interference (ISI) caused by the multipath channel. The proposed novel iterative FDFS‐based receivers firstly reconstruct the received symbol to reduce the ISI and then followed by the FDFS‐based equalisers to minimise the effect of ISI and inter‐carrier interference (ICI) caused by carrier frequency offset (CFO) and Doppler shifts. A few iterations are performed to achieve the expected bit error rate (BER) performance. To reduce the receiver complexity, the novel simplified diagonal FDFS‐based receivers with a fixed noise variance are developed with slight performance degradation. The proposed iterative receivers have never been studied in the existing literature. Simulation results show that the proposed iterative FDFS‐based receivers can significantly improve the BER performance of the conventional FDFS‐MMSE receiver in severe multiple interferences environments caused by multipath, CFO and Doppler shift.

UNLABELLED: Next generation sequencing (NGS) techniques have been widely used to document the small ribonucleic acids (RNAs) implicated in a variety of biological, physiological and pathological processes. An integrated computational tool is needed for handling and analysing the enormous datasets from small RNA deep sequencing approach. Herein, we present a novel web server, CPSS (a computational platform for the analysis of small RNA deep sequencing data), designed to completely annotate and functionally analyse microRNAs (miRNAs) from NGS data on one platform with a single data submission. Small RNA NGS data can be submitted to this server with analysis results being returned in two parts: (i) annotation analysis, which provides the most comprehensive analysis for small RNA transcriptome, including length distribution and genome mapping of sequencing reads, small RNA quantification, prediction of novel miRNAs, identification of differentially expressed miRNAs, piwi-interacting RNAs and other non-coding small RNAs between paired samples and detection of miRNA editing and modifications and (ii) functional analysis, including prediction of miRNA targeted genes by multiple tools, enrichment of gene ontology terms, signalling pathway involvement and protein-protein interaction analysis for the predicted genes. CPSS, a ready-to-use web server that integrates most functions of currently available bioinformatics tools, provides all the information wanted by the majority of users from small RNA deep sequencing datasets. AVAILABILITY: CPSS is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/db/cpss/index.html or http://mcg.ustc.edu.cn/sdap1/cpss/index.html.

Automatic modulation recognition is advantageous for wireless communication systems employing adaptive modulation, software-defined radio, and cognitive radio. In this paper, we consider a phase based maximum likelihood (ML) approach for identifying the modulation format of a linearly modulated signal. Since the optimal ML scheme is computationally intensive, we propose two approximate ML alternatives, which can offer close-to-optimal performance with reduced complexity. We then present a general performance analysis for classification of K types of modulation constellations. For K;5, we obtain a set of upper bounds on Pcc, which provide a tradeoff between accuracy and complexity in calculating the Pcc. In addition, asymptotic behavior of phase based ML classification schemes is investigated.

We consider a phase based maximum likelihood (ML) approach for identifying the modulation format of a linearly modulated signal. Since the optimal ML scheme is computationally intensive, we propose two approximate ML alternatives derived from Gauss quadrature rules. The proposed approximate ML schemes can offer virtually optimal performance with reduced complexity. We then present a general performance analysis for classification of multiple modulation constellations.

Using the semi-infinite Gauss-Hermite quadrature rule defined in (0,∞), we present an accurate and efficient approximation to the Gaussian Q-function, which is expressed as a finite sum of exponential functions. Based on this approximation, we address a product of Gaussian Q-functions averaged over Nakagami-m fading, ending up with a closed-form solution applicable for any real m ≥ 0.5. We further consider more general situations, in which the Gaussian Q-function is involved in more complicated ways. Numerical examples show that the proposed method with very few terms can give error probabilities (in closed form) that are virtually indistinguishable from the exact results obtained by numerical integration.

UNLABELLED: High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species. AVAILABILITY: miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php.

This paper is concerned with classification of quadrature amplitude modulation (QAM) signals by using noncoherent maximum likelihood (ML) schemes, which are robust to frequency mismatch and phase shift. To reduce the high complexity involved in optimal noncoherent ML classification, we propose two suboptimal alternatives that perform well under various conditions. In particular, one suboptimal scheme, derived from high signal-to-noise ratio (SNR) approximation, is shown to provide close-to-optimal performance. We also present a general performance analysis for noncoherent ML classification of K types of QAM constellations, ending up with simplified integration for K; 5. In addition, we investigate the asymptotic behavior of noncoherent ML classification assuming the number of samples used increases to infinity. The asymptotic performance of optimal as well as suboptimal noncoherent ML classification is established.

Abstract Automatic modulation classification (MC) is beneficial to digital radio receivers. Conventional MC schemes usually assume that perfect synchronization has been accomplished and then rely on certain statistical characteristics to distinguish different modulation formats. In practice, however, this perfect synchronization assumption is not reasonable since modulation classifiers operate in a non‐cooperative manner and therefore, the receiver has little prior knowledge about a transmitted signal and no training is available. In this paper, we first address asynchronous MC for high‐order QAMs through blind time synchronization. A characteristic function (CF) based approach is proposed to improve the performance of the conventional cumulant method. We then move on to consider asynchronous MC in the presence of frequency offset. We propose a hybrid MC scheme based on blind time synchronization, differential processing, and cumulants to solve this difficult problem. Copyright © 2010 John Wiley & Sons, Ltd.

We propose to use frequency-domain oversampling (FDO) to efficiently exploit diversity inherent in a multipath channel. Both linear minimum mean square error (LMMSE) and nonlinear Vertical-Bell Laboratories-Layered-Space-Time (VBLAST) receivers are considered. Numerical results show that compared with conventional counterparts without FDO, the proposed receivers yield significantly better uncoded as well as coded symbol error rate (SER) performance. In addition, we find through simulations that an minnimum FDO exists, which is determined by the number of subcarriers and the number of multipath components.

This paper is concerned with amplitude-based maximum likelihood classification for quadrature amplitude modulation formats in the presence of frequency offset. We first propose a simplified classification algorithm that incurs a small loss in performance. A general performance analysis is then elaborated, which can achieve a higher computational efficiency, compared with the existing one. By integrating blind time synchronization and signal-to-noise (SNR) estimation, we further develop a robust classifier that is able to deal with some commonly unknown system parameters including frequency offset, phase shift, time delay, and SNR.

Based on the frequency-domain oversampling and minimum mean-square error (MMSE) principles, we propose three linear single-user detectors for downlink multicarrier codedivision multiple-access (MC-CDMA) systems. We begin with an optimal linear MMSE detector, which is computationally demanding. To reduce the complexity, a two-stage MMSE detector and a diagonal one-stage MMSE detector are developed subsequently. Simulation results show that the proposed detectors can efficiently suppress the multiple access interference (MAI) caused by frequency-selective fading, near-far effect, frequency offset, and nonlinear power amplification.

We consider modulation classification for quadrature amplitude modulation (QAM) formats. The received signal is assumed to be unsynchronized in both time and frequency, since in practice the receiver has little prior knowledge about the transmitted signal. To tackle this challenging problem, we propose a classifier that is based on a combination of blind time synchronization, differential processing, and maximum likelihood (ML) detection. A computationally efficient scheme is then developed. Numerical results are provided to justify our approach.

Conventional modulation classification (MC) schemes usually assume that perfect synchronization has been accomplished and then rely on certain statistical characteristics to distinguish different modulation formats. In practice, however, this perfect synchronization assumption is not reasonable since modulation classifiers operate in a non-cooperative manner and therefore, the receiver has little prior knowledge about the transmitted signals and no training is available. In this paper, we first address asynchronous MC for high-order QAMs through blind time synchronization. A characteristic function (CF) based approach is proposed to improve the performance of the conventional cumulant method. We then move on to consider asynchronous MC with frequency offset. We propose a hybrid MC scheme based on blind time synchronization, differential processing, and cumulants to solve this difficult problem.

The constellation design for differential space-time modulation usually requires to construct L (constellation size) unitary matrices making the design complexity increasing rapidly with L. In this correspondence, we propose a new construction technique, which exploits the rotational invariance of unitary matrices and the property of MPSK modulation. The proposed constellations are derivable from a smaller set of D = L/M unitary matrices (via rotations defined by an M-ary PSK symbol) which, in turn, are parameterized by two unitary matrices U1and U2. They share some good property of group constellations thereby considerably simplifying the optimization of U1and U2. They also possess other good properties which allow for simplified maximum likelihood (ML) decoding. Extensive numerical examples are presented to demonstrate that the proposed constellations in general have larger diversity products than other constellations and thus, better error performance.

This paper concerns the multiple access performance of spreading codes used in the downlink of multicar- rier code-division multiple-access (MC-CDMA). Complex-valued spreading codes, particularly polyphase codes, are investigated based on a system model in which modulation formats, equalization/combining schemes, and fading statistics are general. Different from previous approaches, we pay special attention to the influence of channel correlation on the performance of spreading codes and try to take advantage of the channel correlation in the design of spreading codes. We begin by considering a worst-case frequency-selective fading channel characterized by independent and identically distributed (i.i.d.) fading for all subcarriers. Unitary polyphase spreading codes are proven to be optimal among all polyphase codes for the i.i.d. fading channel. Next, optimal spreading codes for a two-user case are derived analytically. We proceed to consider a block-wise correlated fading channel model. We propose a class of channel-correlation- matched Kronecker-product unitary spreading codes, which can exploit the channel correlation, thereby reducing multiple access interference (MAI) significantly. Finally, we extend the Welch lower bound to the downlink of MC-CDMA over correlated fading channels and investigate the performance of Welch bound equality codes.

Orthogonality restoring combining (ORC) is appealing for the downlink of multicarrier code-division multiple- access (MC-CDMA) since it can eliminate multiple access interference (MAI) which would otherwise fundamentally limit the CDMA system performance. The ORC output instantaneous signal-to-noise ratio (SNR), however, takes the form of a harmonic mean of SNRs on different subcarriers, making its error performance difficult to analyze. In this paper, we tackle the problem in the framework of characteristic functions (CF) ending up with useful results for various modulation schemes including M-ary phase-shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM). The general solution for arbitrary real m can be evaluated numerically, and reduces to closed form for m being an odd multiple of half. Extensive simulations are conducted to verify the theoretical analysis. It is found that in general MQAM outperforms MPSK, and the error performance of MC-CDMA with ORC is insensitive to the number of subcarriers and to the correlation among subcarriers.

We present a new approach to design unitary constellations for differential space-time modulation. By exploiting the rotational invariance of unitary matrices and the property of M-ary PSK symbols, the proposed constellations of size L are derivable from a smaller set of D=L/M unitary matrices (via rotations defined by M-ary PSK symbols) which, in turn, are parameterized by two unitary matrices U/sub 1/ and U/sub 2/. The proposed constellations share some good properties of group constellations thereby considerably simplifying the optimization of U/sub 1/ and U/sub 2/. They also possess many other good properties which, when exploited, allow for simplified maximum likelihood decoding. Extensive numerical examples are provided to demonstrate that the proposed constellations in general have larger diversity products than other constellations.

In the bit-error rate (BER) analysis of code-division multiple-access (CDMA) systems, a Gaussian approximation is widely used to tackle the multiple access interference (MAI), although it does not always offer satisfactory accuracy. This paper investigates the BER performance of synchronous multicarrier (MC) CDMA systems over Nakagami-m-fading channels in a different way. We present an accurate and unified BER analysis for synchronous MC-CDMA systems. To facilitate our analysis, we assume a synchronous uplink, whose BER performance can be intuitively viewed as a lower BER bound of the more realistic asynchronous MC-CDMA. The basic idea is that, by using the Gauss-Chebyshev quadrature (GCQ) rule to perform inverse Laplace transform, an accurate BER can be numerically obtained from the moment generating function (MAG) of the output decision variable at a receiver, without any assumption about the MAI distribution. First, signals on all subcarriers of MC-CDMA systems are assumed to experience independent fading. Two standard diversity combining techniques, equal gain combining (EGC) and maximal ratio combining (MRC), are employed. The BER performance in both downlink and synchronous uplink is analyzed. We then consider a more general system model, in which signals on different subcarriers undergo correlated fading. The asymptotic (error floor) performance of downlink MC-CDMA with MRC is studied. In particular, we investigate the effects of spreading sequences and the delay spread of the channel on the system performance. Numerical examples are provided to show the main results of this paper. The accuracy of the GCQ and MGF based solution is verified by different approaches such as Monte Carlo integration and the exact residue method. In addition, the accuracy of the commonly used Gaussian approximation is also examined.

The bit error rate (BER) of an asynchronous multicarrier code-division multiple-access system in an additive white Gaussian noise channel is evaluated using Monte Carlo integration and moment-generating function methods. The BER performance for different families of spreading sequences is investigated. Numerical results show that the approach of a recently published paper (see Popovic, B.M., IEEE Trans. Common., vol.47, p.918-26, 1999) cannot give an accurate BER because the interference from other subcarriers has been omitted. Some new findings about the performance of different sequences are also presented.