The gene BRCA1 is a human tumor suppressor gene found in all humans, and it was reported that around 5-10% of people with breast cancer have a mutation of their BRCA1 genes. In this study, we investigated potential long non-coding RNA biomarkers for BRCA1 mutation carriers breast cancer cases. From differential expression analysis of RNA-Seq data, we identified 6 significant long non-coding genes (p-value < 0.05). There are three long non-coding genes which were reported as potential biomarkers in breast cancer: AC008268.1, AC091013.1 and AL021395.1. Then the other three genes (AC008592.5, AC090204.1, LINC02570) are novel candidates reported in this study.

The ECI2 gene encodes a isomerase of mammalian peroxisomes, and one of ECI2 isoforms was found as an important cancer antigen called as hepatocellular carcinoma-associated antigen 64 (HCA64). Recently, it was also found as a gene signature for the prognosis of other cancers such as breast cancer and prostate cancer. High-throughput RNA sequencing has become the state-of-the-art method for measuring the levels of gene expression. This paper studies the expression analysis of breast cancer gene signatures with both Hisat2 and RSEM programs from breast cancer RNA-Seq datasets. The results showed that the transcript of HCA64 was only expressed in only part of breast cancer samples.

MOTIVATION: High-accuracy de novo assembly of the short sequencing reads from RNA-Seq technology is very challenging. We introduce a de novo assembly algorithm, EBARDenovo, which stands for Extension, Bridging And Repeat-sensing Denovo. This algorithm uses an efficient chimera-detection function to abrogate the effect of aberrant chimeric reads in RNA-Seq data. RESULTS: EBARDenovo resolves the complications of RNA-Seq assembly arising from sequencing errors, repetitive sequences and aberrant chimeric amplicons. In a series of assembly experiments, our algorithm is the most accurate among the examined programs, including de Bruijn graph assemblers, Trinity and Oases. AVAILABILITY AND IMPLEMENTATION: EBARDenovo is available at http://ebardenovo.sourceforge.net/. This software package (with patent pending) is free of charge for academic use only. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed. RESULTS: In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes. CONCLUSION: In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).

Experimental results of applying two similarity measurements, Euclidean distance and chord distance, to test a set of six Brodatz's textures are reported. Experiments show that in addition to feature extraction, a similarity measurement between images should be simultaneously considered. We also review some other similarity measurements.

Fractal image compression is famous for its particular iterated decoder and the magic Collage theorem. This paper proposes an efficient decoding scheme. In fractal image compression, an image is partitioned into nonoverlapped range blocks and overlapped domain blocks. That is, there are more domains than ranges. Hence many pixels {p/sub i/} in the image do not belong to any domain blocks and these pixels need not be computed iteratively. We can compute them only once in the last iteration. Moreover, some other pixels {p/sub i/} can also be computed noniteratively if they only map to {p/sub i/}. Therefore iterative computations on {p/sub i/} and {p/sub i/} are redundant. We can eliminate the redundancy to accelerate the decoder without any loss on fidelity. In our experiment, the polished procedure can speed up on a large scale. It takes only 0.2-0.3 seconds to decode a 512 by 512 image on a Pentium II 450 PC running Windows 98.

With iterated function systems (IFS), there are two classical algorithms of generating artificial fractal pictures: the deterministic algorithm and the random iteration algorithm. The deterministic algorithm spends huge computations on checking convergence iteratively. On the other hand, the random iteration algorithm is nondeterministic and time consuming. People seldom check convergence by the algorithm. Moreover, many pixels in the picture are computed again and again by both of the algorithms. Thus a new algorithm is given. The algorithm promises convergence without iterative checks. Also, our algorithm runs faster than the existing algorithms do on a large scale.

Summary form only given. Image data compression by fractal techniques has been widely investigated. Although its high compression ratio and resolution-free decoding properties are attractive, the encoding process is computationally demanding in order to achieve an optimal compression. This article proposes a fast fractal-based encoding algorithm (ACC) by using the intensity changes of neighboring pixels to search for a suboptimal domain block for a given range block. Experimental results show that our algorithm achieves close to the optimal algorithm (OPT) for 256/spl times/256 images Jet, Lenna, Mandrill, and Peppers, with a compression ratio of 16. A comparison of the performance of algorithms OPT and ACC on a Sun Ultra 1 Sparc workstation is given.