BACKGROUND: High-throughput sequencing technologies generate massive amounts of FASTQ data comprising nucleotide sequences, quality scores, and read identifiers, necessitating efficient compression to alleviate storage and transmission burdens. Compared to general-purpose compressors, specialized FASTQ compressors achieve higher compression performance by exploiting the inherent redundancy in FASTQ files. However, existing FASTQ-specialized compressors often suffer from limited data applicability and tend to over-optimize either compression ratio or compression speed at the expense of the other. RESULTS: We present zDUR, a reference-free FASTQ compressor designed for efficient and scalable handling of next-generation sequencing data across diverse platforms and sequencing data types. Benchmarking against six reference-free compressors on 15 representative datasets spanning four sequencing data types demonstrates that zDUR achieves a favorable overall balance between compression ratio and speed, with broad applicability across data types. In particular, on single-cell RNA-seq and spatial transcriptomics datasets, zDUR achieves over a tenfold increase in runtime performance while maintaining higher compression ratios than SPRING, one of the state-of-the-art reference-free FASTQ compressors. CONCLUSIONS: zDUR offers a scalable and efficient solution for reference-free FASTQ compression, balancing performance, speed, and usability across diverse datasets.

MOTIVATION: Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS: In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: T and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database. RESULTS: We developed a database, the Pan Immune Repertoire Database (PIRD), located in China National GeneBank (CNGBdb), to collect and store annotated TCR and BCR sequencing data, including from Homo sapiens and other species. In addition to data storage, PIRD also provides functions of data visualization and interactive online analysis. Additionally, a manually curated database of TCRs and BCRs targeting known antigens (TBAdb) was also deposited in PIRD. AVAILABILITY AND IMPLEMENTATION: PIRD can be freely accessed at https://db.cngb.org/pird.

Abstract Fine‐grained image classification is a challenging topic in the field of computer vision. General models based on first‐order local features cannot achieve acceptable performance because the features are not so efficient in capturing fine‐grained difference. A bilinear convolutional neural network (CNN) model exhibits that a second‐order statistical feature is more efficient in capturing fine‐grained difference than a first‐order local feature. However, this framework only considers the extraction of a second‐order feature descriptor, using a single convolutional layer. The potential effective classification features of other convolutional layers are ignored, resulting in loss of recognition accuracy. In this paper, a multilayer feature descriptors fusion CNN model is proposed. It fully considers the second‐order feature descriptors and the first‐order local feature descriptor generated by different layers. Experimental verification was carried out on fine‐grained classification benchmark data sets, CUB‐200‐2011, Stanford Cars, and FGVC‐aircraft. Compared with the bilinear CNN model, the proposed method has improved accuracy by 0.8%, 1.1%, and 5.5%. Compared with the compact bilinear pooling model, there is an accuracy increase of 0.64%, 1.63%, and 1.45%, respectively. In addition, the proposed model effectively uses multiple 1×1 convolution kernels to reduce dimension. The experimental results show that the multilayer low‐dimensional second‐order feature descriptors fusion model has comparable recognition accuracy of the original model.

This paper proposed a high-performance image retrieval framework, which combines the improved feature extraction algorithm SIFT (Scale Invariant Feature Transform), improved feature matching, improved feature coding Fisher and improved Gaussian Mixture Model (GMM) for image retrieval. Aiming at the problem of slow convergence of traditional GMM algorithm, an improved GMM is proposed. This algorithm initializes the GMM by using on-line [Formula: see text]-means clustering method, which improves the convergence speed of the algorithm. At the same time, when the model is updated, the storage space is saved through the improvement of the criteria for matching rules and generating new Gaussian distributions. Aiming at the problem that the dimension of SIFT (Scale Invariant Feature Transform) algorithm is too high, the matching speed is too slow and the matching rate is low, an improved SIFT algorithm is proposed, which preserves the advantages of SIFT algorithm in fuzzy, compression, rotation and scaling invariance advantages, and improves the matching speed, the correct match rate is increased by an average of 40% to 55%. Experiments on a recently released VOC 2012 database and a database of 20 category objects containing 230,800 images showed that the framework had high precision and recall rates and less query time. Compared with the standard image retrieval framework, the improved image retrieval framework can detect the moving target quickly and effectively and has better robustness.