Child

To create the annotations network ClueGO provides predefined functional analysis settings ranging from general to very specific ones. Furthermore, the user can adjust the analysis parameters to focus on terms, e.g. in certain GO level intervals, with particular evidence codes or with a certain number and percentage of associated genes. An optional redundancy reduction feature (Fusion) assesses GO terms in a parent–child relation sharing similar associated genes and preserves the more representative parent or child term. The relationship between the selected terms is defined based on their shared genes in a similar way as described by Huang et al. (2007 (link)). ClueGO creates first a binary gene-term matrix with the selected terms and their associated genes. Based on this matrix, a term–term similarity matrix is calculated using chance corrected kappa statistics to determine the association strength between the terms. Since the term–term matrix is of categorical origin, kappa statistic was found to be the most suitable method. Finally, the created network represents the terms as nodes which are linked based on a predefined kappa score level. The kappa score level threshold can initially be adjusted on a positive scale from 0 to 1 to restrict the network connectivity in a customized way. The size of the nodes reflects the enrichment significance of the terms. The network is automatically laid out using the Organic layout algorithm supported by Cytoscape. The functional groups are created by iterative merging of initially defined groups based on the predefined kappa score threshold. The final groups are fixed or randomly colored and overlaid with the network. Functional groups represented by their most significant (leading) term are visualized in the network providing an insightful view of their interrelations. Also other ways of selecting the group leading term, e.g. based on the number or percentage of genes per term are provided. As an alternative to the kappa score grouping the GO hierarchy using parent–child relationships can be used to create functional groups.
When comparing two gene clusters, another original feature of ClueGO allows to switch the visualization of the groups on the network to the cluster distribution over the terms. Besides the network, ClueGO provides overview charts showing the groups and their leading term as well as detailed term histograms for both, cluster specific and common terms.
Like BiNGO, ClueGO can be used in conjuntion with GOlorize for functional analysis of a Cytoscape gene network. The created networks, charts and analysis results can be saved as project in a specified folder and used for further analysis.

fastp supports automatic adapter trimming for both single-end and paired-end Illumina data and uses different algorithms for each of these tasks. For single-end data, adapter sequences are detected by assembling the high-frequency read tails; for paired-end data, adapter sequences are detected by finding the overlap of each pair.
The adapter-sequence detection algorithm is based on two assumptions: the first is that only one adapter exists in the data; the second is that adapter sequences exist only in the read tails. These two assumptions are valid for major next-generation sequencers like Illumina HiSeq series, NextSeq series and NovaSeq series. We compute the k-mer (k = 10) of first N reads (N = 1 M). From this k-mer, the sequences with high occurrence frequencies (>0.0001) are considered as adapter seeds. Low-complexity sequences are removed because they are usually caused by sequencing artifacts. The adapter seeds are sorted by its occurrence frequencies. A tree-based algorithm is applied to extend the adapter seeds to find the real complete adapter, which is described by the pseudo code in Algorithm 1.
In Algorithm 1, the function build_nucleotide_tree() is used to convert a set of sequences to a tree, in which each node is a nucleotide and each path of root to leaf is a sequence. A node’s dominant child is defined as its major child with a dominant percentage (>90%). This algorithm tries to extend an adapter seed in the forward direction to check its validity since a valid adapter can always be extended to the read tails. And if this adapter seed is valid, a backward extension is applied to obtain the complete adapter sequence. The process of extending an adapter seed in forward and backward directions is given in Figure 2.
For paired-end data, fastp seeks the overlap of each pair and considers the bases that fall out of the overlapped regions as adapter contents. The overlapping detection algorithm was derived from our previous work, AfterQC. Compared to sequence-matching-based adapter-trimming tools like Cutadapt and Trimmomatic, a clear advantage of the overlap-analysis-based method is that it can trim adapters with few bases in the read tail. For example, most sequence-matching-based tools require a hatchment of at least three bases and cannot trim adapters with only one or two bases. In contrast, fastp can trim adapters with even only one base in the tail.
Although fastp can detect adapter sequences automatically, it also provides interfaces to set specific adapter sequences for trimming. For SE data, if an adapter sequence is given, then automatic adapter-sequence detection will be disabled. For PE data, the adapter sequence will be used for sequence-matching-based adapter trimming only when fastp fails to detect a good overlap in the pair.