PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data

Motivation Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes, and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock, and collapsing bipartitions (internal branches) with low support. Results To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene-gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes; and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining, and deriving biological meaning from increasingly large phylogenomic datasets. Availability PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/), and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT). Supplementary information Supplementary data are available on figshare (doi: 10.6084/m9.figshare.13118600) and are available at Bioinformatics online.

PhyKIT: a UNIX shell toolkit for processing and analyzing phylogenomic data

Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes, and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock, and collapsing bipartitions (internal branches) with low support. To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene-gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes; and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining, and deriving biological meaning from increasingly large phylogenomic datasets. PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT) and documentation including user tutorials are available online (https://jlsteenwyk.com/PhyKIT).

Expression profile and co-expression analysis of ADH1A in human solid tumors.

e13533 Background: The human alcohol dehydrogenase (ADH) gene family is associated with various solid cancers. It seems that the ADH1 gene cluster plays an important role in various solid tumors, so it aroused our interest in studying these genes to find out their functions and biological process within different solid tumors. Methods: Paired tumor and normal tissues gathered from 38 tumor patients, and 5 male BALB/c mice tissues were collected and Immunohistochemistry (IHC) assay was performed. The expression of ADH1A at RNA level in normal tissues and pan-solid tumors and the main functions of ADH1A in different solid tumors were analyzed by Bioinformatics mining. Results: At the RNA level, ADH1A was highly expressed in normal hepatocytes and was expressed lower in the tumor tissues than in the adjacent normal tissues or the corresponding normal tissues, suggesting the At the protein level, ADH1A was expressed to varying degrees in human alveoli, kidney, stomach, colon, and rectum. We predicted three major conserved functions of ADH1A, including angiogenesis, cell adhesion, and leukocyte migration function which might influence the prognosis of the immunotherapy and the immune response, and constructed an upstream regulation network of ADH1A and a downstream protein network. Besides, we also explored the functional differences of ADH1A in lung adenocarcinoma and lung squamous cell carcinoma and its effect on overall survival. And for investigating ADH1A, the BALB/c mice might be an option to consider in constructing an animal model of gastric cancer (GC), esophageal carcinoma (ESCA), liver hepatocellular carcinoma (LIHC), and pancreatic adenocarcinoma (PAAD). Conclusions: ADH1A has potential diagnostic and prognostic value in various solid tumors. Our findings highlight new avenues for further investigation of ADH1A biology process and provide a novel potential prognostic biomarker of immunotherapy.

Autophagy in Immune-Related Renal Disease

Autophagy is an important biology process, central to the maintenance of biology process in both physiological and pathological situations. It is regarded as a “double-edged sword”—exerting both protective and/or detrimental effects. These two-way effects are observed in immune cells as well as renal resident cells, including podocytes, mesangial cells, tubular epithelial cells, and endothelial cells of the glomerular capillaries. Mounting evidence suggests that autophagy is implicated in the pathological process of various immune-related renal diseases (IRRDs) as well as the kidney that underwent transplantation. Here, we provide an overview of the pathological role of autophagy in IRRDs, including lupus nephritis, IgA nephropathy, membrane nephropathy, ANCA-associated nephritis, and diabetic nephropathy. The understanding of the pathogenesis and regulatory mechanisms of autophagy in these renal diseases may lead to the identification of new diagnostic targets and refined therapeutic modulation.

Salingtemas Approach (Science, Environment, Technology And Society) in Biology Learning

SALINGTEMAS approach is a combination of STS (Science, Technology and Society) and EE (Environmental Education).In abroad, the approach is known as SALINGTEMAS (Science, Education, Technology and Society) otherwise in Indonesia,it is known as SALINGTEMAS. The main characteristic of SALINGTEMAS is learning something based on the problemsand the issues faced by students in everyday life that contains components of science and technology. SALINGTEMAS triesto provide an understanding of the role of the environment on science, technology and society, and the other way. There arefour phases in learning using SALINGTEMAS approach, namely: invitation phase, exploration, suggest of explanations andsolutions as well as take action. In the teaching biology process, this approach is associated with the elements ofenvironment, technology and society integratively. There are so many topics or biological problems that can be solved usingSALINGTEMAS approach

Next generation tools to accelerate the synthetic biology process

We highlight recently developed microfluidic tools that can be used to automate the synthetic biology workflow with the goal of advancing the likelihood of producing desired functionalities.

PENGGUNAAN MODEL PEMBELAJARAN KOOPERATIF TIPE NUMBERED HEADS TOGETHER (NHT) UNTUK MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR BIOLOGI KELAS VII-1 SMPN 25 PEKANBARU

This research focuses on an effort to look improvement of activities and a biology subject result as an effect of implemention of Numbered Head Together (NHT) in learning. This research was done at SMP N 25 Pekanbaru on biology subject, with subject was 40 students of class VII-1, 19 females and 21 males. After this research has been done, the average students’ activities increased on activity of listening teacher’s explanation from 90% in the first cycle into 94 % in the second cycle. Then it was a proof that learning biology process with using Cooperatif Learning Model Type Number Heads Together can increase students’ participantion in learning process on classroom discussion. Meanwhile, students’ result on Biology learning, Animal Classification, in class VII-1 on SMPN 25 Pekanbaru using Cooperative Learning Model Type Numbered Heads Together increased from first to second cycle. In can be seen from thoroughness percentage of students who achieved above school standar, KKM, namely 27 person (68%) on the first cycle and 34 person (85%) on the second cycle, or it increased 17%.Keywords: numbered heads together, learning activity, learning result.