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使用 Homebrew 安装 badread

查看 badread 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。

安装

其他安装命令

macOS

Homebrew已验证 · 100%
brew install badread

local Homebrew formula metadata

概览

软件包摘要

Long read simulator that can imitate many types of read problems

命令和别名

  • badread

历史

项目历史与用法

Badread is a bioinformatics CLI for simulating error-prone long sequencing reads. It is historically notable less as a large software platform and more as a citable research tool that gave developers controlled ways to stress-test long-read assemblers and analysis pipelines.

项目历史

The Badread GitHub repository was created in June 2018, with the first v0.1.0 GitHub release in July 2018. The README says Badread was made to test tools that take long reads as input by letting users control read problems such as chimeras, low-quality regions, systematic basecalling errors, junk reads, random reads, adapters, glitches, and quality-score models.

Badread was published in the Journal of Open Source Software in 2019 as 'Badread: simulation of error-prone long reads' with DOI 10.21105/joss.01316. That gave the package a stable academic citation path alongside its command-line distribution.

The project continued to track long-read practice in later releases, with README examples for older Oxford Nanopore reads, newer Nanopore R10.4.1-style settings, PacBio HiFi-style reads, and configurable error and qscore models.

采用历史

Badread's adoption is mainly in computational biology workflows where developers need reproducible fake FASTQ data. Its packaging in Homebrew makes it easy for macOS bioinformatics users to install without manually cloning the repository, while the README also documents pip installation directly from GitHub.

The JOSS publication and Zenodo DOI made Badread easier to cite in papers and benchmarking notes than many informal simulator scripts. GitHub release activity from 2018 through 2026 shows a maintained niche tool rather than a frozen paper artifact.

使用方式

Typical usage is badread simulate with a reference FASTA and requested quantity, piping FASTQ output through gzip. Users tune read length, identity, error model, qscore model, adapter sequences, chimeras, glitches, junk reads, random reads, and seeds.

The README emphasizes control over realism: users can deliberately make reads very bad, pretty good, very good, or platform-like in order to test how downstream tools react.

为什么软件包爱好者会关心

Badread matters to package nerds because it is a compact example of research software that deserves normal CLI packaging: it has a paper, DOI, reproducible command-line interface, domain data models, and a long tail of users who may just need the executable in a workflow.

It also shows why scientific packages often live awkwardly between GitHub, pip, Homebrew, and citation systems: the code, docs, releases, and scholarly identity all matter.

时间线

  • 2018: Badread GitHub repository created and v0.1.0 released.
  • 2019: Badread published in the Journal of Open Source Software.
  • 2021: v0.2.0 released.
  • 2023: v0.3.0 and v0.4.0 released.
  • 2026: v0.4.2 released.

Related projects

  • Badread is related to long-read sequencing platforms and models such as Oxford Nanopore and PacBio.
  • The README compares Badread with other long-read simulators and notes dependencies such as Edlib, NumPy, SciPy, and Matplotlib.

安全态势

风险级别:绿色

narrow executable package without higher-risk signals.

风险分类器

绿色 风险 · 低 置信度 · appliance

原因

  • narrow executable package without higher-risk signals

信号

  • metadata:no-higher-risk-signals

安装行为

  • 未记录 Homebrew bottle 元数据。

建议审查

在无人值守的代理使用前,请检查该工具是否读取明文凭据、写入远程状态、发布制品或调用插件。

可执行文件

已安装的可执行文件

命令类型暴露范围备注
badread可执行文件已索引可执行文件从本地可执行文件索引发现。

新鲜度

版本和新鲜度

这些信号区分页生成时间、软件包管理器活动和上游发布比较。只有存在证据 URL 和可比较版本时,才会提示版本落后。

页面生成时间2026-08-03
管理器版本0.4.2
管理器更新时间
本地数据未知
上游不可用
检测到的最新版本未检测到
  • OK没有生成新鲜度警告。

安装元数据

软件包元数据

软件包键brew:badread
版本0.4.2
软件包管理器Homebrew
主页https://github.com/rrwick/Badread
仓库https://github.com/rrwick/Badread
Bottle未记录
服务未声明

来源线索

由仓库数据生成

此页面由 av-webscripts/generate-pkg-sqlite.py 生成的私有软件包 SQLite 工件提供。

使用的来源

  • Geiger risk classifier
  • cross-ecosystem install command graph
  • curated package history
  • pkg.so package database
  • pkgdb category and tag curation