Search

Internal tandem duplication (ITD) of FMS-like tyrosine kinase 3 (FLT3-ITD) constitutes an independent indicator of poor prognosis in acute myeloid leukaemia (AML). AML with FLT3-ITD usually presents with poor treatment outcomes, high recurrence rate and short overall survival. Currently, polymerase chain reaction and capillary electrophoresis are widely adopted for the clinical detection of FLT3-ITD, whereas the length and mutation frequency of ITD are evaluated using fragment analysis. With the development of sequencing technology and the high incidence of FLT3-ITD mutations, a multitude of bioinformatics tools and pipelines have been developed to detect FLT3-ITD using next-generation sequencing data. However, systematic comparison and evaluation of the methods or software have not been performed. In this study, we provided a comprehensive review of the principles, functionality and limitations of the existing methods for detecting FLT3-ITD. We further compared the qualitative and quantitative detection capabilities of six representative tools using simulated and biological data. Our results will provide practical guidance for researchers and clinicians to select the appropriate FLT3-ITD detection tools and highlight the direction of future developments in this field. Availability: A Docker image with several programs pre-installed is available at https://github.com/niu-lab/docker-flt3-itd to facilitate the application of FLT3-ITD detection tools.

Motivation: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circulating cell-free DNA (cfNDA) technology as a noninvasive MSI detection approach. Results: We developed MSIsensor-ct, a bioinformatics tool based on a machine learning protocol, dedicated to detecting MSI status using cfDNA sequencing data with a potential stable MSIscore threshold of 20%. Evaluation of MSIsensor-ct on independent testing datasets with various levels of circulating tumor DNA (ctDNA) and sequencing depth showed 100% accuracy within the limit of detection (LOD) of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, rendering it user-friendly and readily integrated into next generation sequencing (NGS) analysis pipelines. Availability: MSIsensor-ct is freely available at https://github.com/niu-lab/MSIsensor-ct. Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

The unstable sensitivity of growth-climate relationships greatly restricts tree-ring-based paleoclimate reconstructions, especially in areas with frequent divergence problems, such as the temperate zone in northeast China. Here, we propose an original tree-species mixing method to overcome this obstacle and improve the stability and reliability of reconstruction models. We take the tree-ring based growing-season minimum temperature reconstruction for the northern Changbai Mountains in northeast China as an example to illustrate the method. Compared with previous temperature reconstruction models, our reconstruction model is more stable and reliable and explains up to 68 % of the variance. It is also highly consistent with historical records and tree-ring-based temperature reconstructions from the nearby Xiaoxing'an Mountains and from across the Northern Hemisphere. Our reconstruction uses two different tree species and is more accurate than temperature reconstructions developed from a single species. Over the past 259 years (AD 1757–2015), five significant cold periods and five warm periods were identified. The reconstruction indicates rapid warming since the 1980s, which is consistent with other instrumental and reconstructed records. We also found the Atlantic Multidecadal Oscillation plays a crucial role in driving the growing-season minimum temperature in the northern Changbai Mountains.

Next-generation sequencing (NGS) technology has revolutionised human cancer research, particularly via detection of genomic variants with its ultra-high-throughput sequencing and increasing affordability. However, the inundation of rich cancer genomics data has resulted in significant challenges in its exploration and translation into biological insights. One of the difficulties in cancer genome sequencing is software selection. Currently, multiple tools are widely used to process NGS data in four stages: raw sequence data pre-processing and quality control (QC), sequence alignment, variant calling and annotation and visualisation. However, the differences between these NGS tools, including their installation, merits, drawbacks and application, have not been fully appreciated. Therefore, a systematic review of the functionality and performance of NGS tools is required to provide cancer researchers with guidance on software and strategy selection. Another challenge is the multidimensional QC of sequencing data because QC can not only report varied sequence data characteristics but also reveal deviations in diverse features and is essential for a meaningful and successful study. However, monitoring of QC metrics in specific steps including alignment and variant calling is neglected in certain pipelines such as the ‘Best Practices Workflows’ in GATK. In this review, we investigated the most widely used software for the fundamental analysis and QC of cancer genome sequencing data and provided instructions for selecting the most appropriate software and pipelines to ensure precise and efficient conclusions. We further discussed the prospects and new research directions for cancer genomics.

Temperate forests in northeast Asia are crucial to maintaining biodiversity conservation and ecological security. Under the background of global warming and drought, it is of great significance to study the special indicator effect of tree growth on climate change. Dendrochronological analysis of different tree species in the same community under climate change can provide valuable information for the adaptive potential of tree species and the species-specific growth indications. Here, we compared the radial growth patterns of 13 co-occurring tree species in a temperate forest community in northeast China. Pearson correlation and moving interval analysis were used to reveal the key climate factors affecting radial growth and the temporal stability of growth-climate relationships, respectively. Results showed that temperature and moisture played a key role in the radial growth of the 13 co-occurring tree species in northeast China. The radial growth of Pinus sylvestris var. mongolica, Picea koraiensis, and Ulmus davidiana increased significantly after rapid warming (around the 1980s), while the radial growth of Pinus koraiensis, Acer mono, and Betula platyphylla decreased slightly. The radial growth of almost all tree species (except P. koraiensis, A. mono, and B. platyphylla) had a positive indication of temperature, especially Juglans mandshurica, Fraxinus mandshurica, and U. davidiana. The precipitation in the early growing season (May to June) and the relative humidity in the growing season were positively correlated with the growth of most species. Coniferous and diffuse-porous tree species were more resistant and recovery to extreme droughts than semi-ring-porous and ring-porous species (except U. davidiana). The 13 co-occurring tree species, especially conifers, showed low resistance and high recovery to extreme temperature. The differences in the growth-climate relationships among different species may be related to wood properties and ecological habits. If the climate continues to warm, coniferous trees will suffer the most serious impact, so they need protection most. Our results highlight that differentiated strategies are needed for the management and restoration of different tree species due to the species-specific indications of climate warming.

Xylem anatomy can provide valuable information about tree allometry and ecophysiological performance. It has unique advantages in the study of global climate change and forest adaptation compared to traditional ring-width or density proxy parameters. Developing appropriate detrended sequences of xylem features is necessary to carry out a series of further studies. However, the differences between detrending methods and their effects for xylem features are still unclear, presenting a challenge to the choice of proper detrending methods. In this study, we used the earlywood vessel features of Fraxinus mandshurica as an example and compared the differences in four common detrending methods (Spline, CSS: cubic smoothing spline, NEP: negative exponential curve, and RCS: regional curve standardization) to highlight the importance of the detrending methods in dendroanatomical studies. The results show that detrending methods significantly affect the long-term growth trends and climate signals recorded in xylem anatomical features. Among them, NEP has good performance in terms of ring width and vessel number, which is not suitable for other vessel features because of its deterministic nature. Spline and CSS are relatively conservative methods, in which are not easy to make mistakes, but the problem of overfitting (lack of climate signals) deserves attention. Multiple RCS may be a promising detrending method with quantitative wood anatomy technology development if sufficient samples are available. Our results highlight the importance of selecting detrending methods for xylem feature identification and emphasize the importance of testing various detrending methods rather than arbitrarily choosing a standard method. The detrending method should be selected based on the purpose, such as the balance between climate signals and thorough detrended effects. We recommend applying multiple methods to test the growth trends in xylem features before selecting a detrending method. More interdisciplinary studies involving ecophysiology, wood anatomy, and dendrochronology will help to distil the valuable information in xylem features accurately.

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.